Science, Faculty of
Chemistry, Department of
DSpace
UBCV
Christensen, Melodie Deniz
Doctor of Philosophy - PhD
University of British Columbia
Autonomous process optimization involves the human-intervention free search of an input parameter space in order to minimize or maximize output parameters of interest. These systems automate the repetitive and manual approach to process optimization that currently dominates the field. The field of autonomous process optimization is in its infancy and a majority of examples have focused on flow reactor-based systems, however, not all processes can be executed in flow reactors due to heterogeneity or long reaction times. We extended this novel technology to high-throughput microscale batch reactors through the integration of commercial Chemspeed robotics platforms with online analytical instruments and optimization algorithms using a Python interface. The first system was applied to the optimization of a stereoselective Suzuki-Miyaura process, and the second to the optimization of a Wohl-Ziegler photobromination process. We found that optimization performance was impacted by the definition of the search space, the appropriate representation of chemical structure, the optimization algorithm, the acquisition function and the selection of the appropriate reactor and analysis technology. These aspects are discussed in detail for each case study. Finally, the analysis of multivariate optimization data proved to be a challenging proposition, and we found that qualitative machine learning approaches such as random forest modeling allowed for the determination of parameter influences in an effective way. The strategy that we have implemented for autonomous process optimization has the potential to be scaled to additional robotic and analytical instruments, as well as optimization algorithms, through its modular Python interface. We envision a future where the utilization of such systems is commonplace.
THE DEVELOPMENT AND APPLICATIONS OF AUTONOMOUS PROCESS OPTIMIZATION SYSTEMS by Melodie Deniz Christensen B.Sc., American University, 2002 M.S., American University, 2003 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Chemistry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2022 © Melodie Christensen, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The Development and Applications of Autonomous Process Optimization Systems submitted by Melodie Christensen in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemistry Examining Committee: Jason Hein, Associate Professor, Chemistry, UBC Supervisor Gregory Dake, Associate Professor, Chemistry, UBC Supervisory Committee Member Michael Wolf, Professor & Head, Chemistry, UBC University Examiner Jeff Clune, Associate Professor, Computer Science, UBC University Examiner Additional Supervisory Committee Members: Laurel Schafer, Professor, Chemistry, UBC Supervisory Committee Member Stephen Withers, Professor, Chemistry, UBC Supervisory Committee Member iii Abstract Autonomous process optimization involves the human-intervention free search of an input parameter space in order to minimize or maximize output parameters of interest. These systems automate the repetitive and manual approach to process optimization that currently dominates the field. The field of autonomous process optimization is in its infancy and a majority of examples have focused on flow reactor-based systems, however, not all processes can be executed in flow reactors due to heterogeneity or long reaction times. We extended this novel technology to high-throughput microscale batch reactors through the integration of commercial Chemspeed robotics platforms with online analytical instruments and optimization algorithms using a Python interface. The first system was applied to the optimization of a stereoselective Suzuki-Miyaura process, and the second to the optimization of a Wohl-Ziegler photobromination process. We found that optimization performance was impacted by the definition of the search space, the appropriate representation of chemical structure, the optimization algorithm, the acquisition function and the selection of the appropriate reactor and analysis technology. These aspects are discussed in detail for each case study. Finally, the analysis of multivariate optimization data proved to be a challenging proposition, and we found that qualitative machine learning approaches such as random forest modeling allowed for the determination of parameter influences in an effective way. The strategy that we have implemented for autonomous process optimization has the potential to be scaled to additional robotic and analytical instruments, as well as optimization algorithms, through its modular Python interface. We envision a future where the utilization of such systems is commonplace. iv Lay Summary Chemical process optimization is a field in industrial chemistry that optimizes the “recipes,” or processes for synthesizing chemicals on a large scale for public use. Despite recent advancements in artificial intelligence, data science and automation, this optimization work is still carried out in a repetitive and manual fashion. Herein, we discuss the development of autonomous, self-optimizing robotics systems that guide chemical process optimization in high throughput, with minimal human intervention. We accomplished this through integrating commercial robotics systems with optimization algorithms and analytical systems designed to measure desired process outcomes. We applied these systems to the process optimization of two case studies, and demonstrated that reasonable optima were reached. The framework that we developed can easily be scaled to any commercial robotic system, enabling scientists across the globe to focus their efforts on problems more challenging than routine process optimization. v Preface The first half of Chapter 1 has not been previously published and the second half was adapted from the following perpective article: • Christensen, M.; Yunker, L. P. E.; Shiri, P.; Zepel, T.; Prieto, P. L.; Grunert, S.; Bork, F.; Hein, J. E. Automation Isn’t Automatic. Chem. Sci. 2021, 12 (47), 15473–15490.1 This perspective was conceived and written by myself and Prof Jason Hein. Various excerpts of this perspective were contributed by co-authors with the relevant subject matter expertise and the manuscript structure and original artworks were created by Zepel, T. and Prieto, P. L. Chapter 2 was adapted from the following two research publications: • Christensen, M.; Yunker, L. P. E.; Adedeji, F.; Häse, F.; Roch, L. M.; Gensch, T.; dos Passos Gomes, G.; Zepel, T.; Sigman, M. S.; Aspuru-Guzik, A.; Hein, J. E. Data-Science Driven Autonomous Process Optimization. Commun. Chem. 2021, 4 (1), 1–12.2 • Christensen, M.; Adedeji, F.; Grosser, S.; Zawatzky, K.; Ji, Y.; Liu, J.; Jurica, J. A.; Naber, J. R.; Hein, J. E. Development of an Automated Kinetic Profiling System with Online HPLC for Reaction Optimization. React. Chem. Eng. 2019, 4 (9), 1555–1558.3 Both projects were conceived of and led by myself. The Suzuki-Miyaura autonomous process optimization project was carried out under the guidance of Profs Matthew S. Sigman, Alán Aspuru-Guzik and Jason E. Hein. The reaction profiling experiments, autonomous protocol development, robotic platform customization, autonomous experimental execution, data analysis, data visualization, compound synthesis and compound vi characterization was carried out by myself. The Python framework for data integration was developed by Yunker, L. P. E. The autonomous protocol development was assisted by Adedeji, F. The ChemOS experimental planning framework and optimization algorithms (Phoenics, Gryffin and Chimera) were developed by Häse, F. and Roch, L. M. The phosphine molecular feature computations, training set selection and predictive modeling were executed by Gensch, T. and dos Passos Gomes, G. The Plotly visualizations were created by Zepel, T. The online HPLC system development was carried out under the guidance of Prof Jason E. Hein, with support from Jurica, J. A. and Naber, J. R. The HPLC integration was assisted by Adedeji, F., Grosser, S. and Zawatzky, K. Side product isolation was exectued by Liu, J. and NMR elucidation was executed by Ji, Y. Chapter 3 has not been previously published. This Wohl-Ziegler autonomous process optimization project was led my myself under the guidance of Prof Jason E. Hein. The high-throughput reaction profiling experiments, autonomous protocol development, robotic platform customization, photoreactor characterization, autonomous experimental execution, data analysis, data visualization and compound characterization was carried out by myself. The materials were synthesized by Fier, P. S. and DiMaso, M. J. The flow scale-up experiment was executed by DiMaso, M. J. The LED NMR studies were carried out by Ji, Y. The TCR photoreactor was developed by Isom, D. and Dabrowski, M. from Analytical Sales in collabroation with Sun, A. C. and myself. The TCR photoreactor integration was exectued by Shay T. from Chemspeed Technologies in collaboration with Sun, A. C. The Python interface code was developed by Kwan, E. E. The optimization algorithms, plateau detection algorithms, optimization performance box plots, MDS trajectory analyses, optimization simulations and RF analyses were carried out by Xu, Y. and Liaw, A. vii The automated experimental work presented in Chapters 2 and 3 were carried out by myself from the MSD Process Research Labs in Rahway, NJ. My time was split equally between my professional work with MSD Process Research and my academic research on behalf of the University of British Columbia, under the remote guidance of Prof Jason E. Hein. I provided verbal research updates to Prof Hein on a weekly basis through individual phone conversations and formal presentations on a biweekly basis through virtual subgroup meetings. I coordinated the team of data scientists and coders through regular meetings and correspondence. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiv List of Figures ...............................................................................................................................xv List of Schemes ......................................................................................................................... xxiii List of Abbreviations ............................................................................................................... xxiv Acknowledgements ................................................................................................................. xxvii Dedication ................................................................................................................................. xxix Chapter 1: Introduction ................................................................................................................1 1.1 Introductory remarks ....................................................................................................... 1 1.2 Overview ......................................................................................................................... 3 1.3 Autonomous chemical process optimization .................................................................. 4 1.3.1 Input parameters.......................................................................................................... 5 1.3.1.1 Continuous, categorical and discrete parameters ................................................ 5 1.3.1.2 Chemical parameter representation ..................................................................... 6 1.3.1.2.1 Non-molecular representation ....................................................................... 7 1.3.1.2.2 Molecular representation ............................................................................... 8 1.3.1.2.3 Chemical intuition based categorical parameter selection .......................... 13 1.3.1.2.4 Machine learning based categorical parameter selection ............................ 13 ix 1.3.2 Optimization Algorithms .......................................................................................... 14 1.3.3 Output Parameters ..................................................................................................... 17 1.3.4 APO in Flow Chemistry............................................................................................ 18 1.3.5 APO in Batch Chemistry .......................................................................................... 23 1.4 Batch Chemistry Automation in the Chemical Sciences .............................................. 23 1.4.1 Equipment Considerations ........................................................................................ 24 1.4.1.1 Solid and liquid handling .................................................................................. 25 1.4.1.2 Stirring and temperature control ....................................................................... 29 1.4.1.3 Translocation..................................................................................................... 32 1.4.1.4 Integrated Platforms .......................................................................................... 34 1.4.2 Experimental Considerations .................................................................................... 35 1.4.2.1 Throughput and scale ........................................................................................ 35 1.4.2.2 Chemical handling and compatibility ............................................................... 40 1.4.2.3 Sampling and analysis....................................................................................... 43 1.4.3 Data Considerations .................................................................................................. 47 1.4.3.1 Equipment control and scheduling .................................................................... 47 1.4.3.2 Data flow ........................................................................................................... 49 Chapter 2: Autonomous optimization of a stereoselective Suzuki-Miyaura reaction ...........50 2.1 Introduction ................................................................................................................... 50 2.1.1 The Suzuki-Miyaura reaction ................................................................................... 53 2.1.2 Suzuki-Miyaura reactions of alkenyl sulfonate electrophiles ................................... 54 2.1.3 The general mechanism of Suzuki-Miyaura reactions with alkenyl electrophiles ... 55 2.1.4 Stereoinversion in Suzuki-Miyaura reactions with alkenyl electrophiles ................. 56 x 2.2 Reaction profiling studies of a stereoselective Suzuki-Miyaura reaction ..................... 60 2.3 Development of a high-throughput autonomous optimizer with end point analysis .... 62 2.3.1 Reactor selection and sampling strategy ................................................................... 62 2.3.2 Optimization algorithm selection .............................................................................. 63 2.3.3 Integration of the closed-loop system ....................................................................... 64 2.3.4 Definition of the search space ................................................................................... 65 2.3.5 Planning the experimental workflow ........................................................................ 67 2.3.6 Reproducibility experiments ..................................................................................... 68 2.4 Autonomous optimization results ................................................................................. 69 2.4.1 Autonomous optimization with ligands selected through chemical intuition ........... 69 2.4.2 Autonomous optimization with ligands selected through molecular features .......... 72 2.5 Evaluation of E-selective ligands identified through predictive modeling ................... 78 2.6 Assessment of optimization performance ..................................................................... 81 2.7 Qualitative modeling to determine process parameter influences ................................ 83 2.8 Conclusion .................................................................................................................... 87 2.9 Methods......................................................................................................................... 90 2.9.1 General ...................................................................................................................... 90 2.9.2 Equipment ................................................................................................................. 90 2.9.3 Procedures ................................................................................................................. 91 2.9.3.1 Representative procedure for reaction profiling studies ................................... 91 2.9.3.2 Representative procedure for test reactions ...................................................... 91 2.9.3.3 Representative procedure for optimization reactions ....................................... 92 2.9.3.4 Representative procedure for online HPLC analysis ........................................ 93 xi 2.9.3.5 Representative procedure for manual reactions ................................................ 93 2.9.3.6 Procedures for the synthesis of noncommercial compounds ............................ 94 Chapter 3: Autonomous optimization of a visible light-promoted Wohl-Ziegler bromination reaction ...................................................................................................................99 3.1 Introduction ................................................................................................................... 99 3.1.1 The Wohl-Ziegler bromination reaction ................................................................. 102 3.1.2 Wohl-Ziegler photobrominations in the synthesis of pharmaceutical compounds . 104 3.2 High-throughput reaction profiling studies to narrow the search space ..................... 107 3.3 LED NMR studies to understand reaction plateauing ................................................ 115 3.4 Development of a high-throughput autonomous photochemistry optimizer .............. 120 3.4.1 Photoreactor development ...................................................................................... 121 3.4.2 UPLC integration .................................................................................................... 124 3.4.3 Plateau detection algorithm for dynamic reaction end point detection ................... 124 3.4.3.1 Algorithm 1 (maximum negative difference) ................................................. 125 3.4.3.2 Algorithm 2 (plateau variability) .................................................................... 125 3.4.3.3 Algorithm 3 (threshold of slope)..................................................................... 125 3.4.4 Optimization algorithm selection ............................................................................ 127 3.4.5 Integration of the closed-loop system ..................................................................... 129 3.5 Definition of the search space ..................................................................................... 130 3.6 Reproducibility experiments ....................................................................................... 133 3.7 Autonomous optimization results ............................................................................... 135 3.8 Assessment of optimization performance ................................................................... 145 3.9 Qualitative modeling to determine process parameter influences .............................. 151 xii 3.10 Conclusion .................................................................................................................. 153 3.11 Methods....................................................................................................................... 154 3.11.1 General ................................................................................................................ 154 3.11.2 Equipment ........................................................................................................... 154 3.11.3 Procedures ........................................................................................................... 155 3.11.3.1 Representative procedure for high throughput reaction profiling experiments 155 3.11.3.2 Representative procedure for LED NMR experiments ............................... 155 3.11.3.3 Procedure for scale-up experiment in flow ................................................. 156 3.11.3.4 Representative procedure for autonomous reproducibility experiments .... 156 3.11.3.5 Representative procedure for autonomous optimization reactions ............. 157 3.11.3.6 Procedures for the synthesis of noncommercial compounds ...................... 158 Chapter 4: Conclusion ...............................................................................................................161 References ...................................................................................................................................167 Appendices ..................................................................................................................................183 Appendix A - Chapter 2 .......................................................................................................... 183 A.1 Supplementary Figures ........................................................................................... 183 A.2 Spectral data for compounds 2.1, 2.3, 2.4 and L20. ............................................... 184 A.3 Analytical methods ................................................................................................. 189 A.4 Calibration curves ................................................................................................... 191 A.5 Chemspeed protocol................................................................................................ 194 A.6 Phosphine feature computations and training set selection .................................... 196 A.7 Predictive modeling ................................................................................................ 200 xiii Appendix B - Chapter 3 .......................................................................................................... 203 B.1 Supplementary Figures ........................................................................................... 203 B.2 Spectral data for compounds 3.1, 3.2 and 3.3. ........................................................ 210 B.3 Analytical methods ................................................................................................. 214 B.4 Chemspeed protocol................................................................................................ 216 xiv List of Tables Table 2. 1. Input and output parameters for optimization. ............................................................ 66 Table 3. 1. Input and output parameters for optimization. .......................................................... 132 Table 3. 2. Eight acids included in the APO listed in order of increasing first pKa (H2O). ....... 132 Table 3. 3. Stock solution concentrations for reproducibility testing. ........................................ 134 Table A.4. 1. HPLC-UV method for yield determination .......................................................... 189 Table A.5. 1. Weight % purity calculation for 2.1 ...................................................................... 191 Table A.5. 2. Weight % purity calculation for 2.3 ...................................................................... 191 Table A.5. 3. Weight % purity calculation for 2.4 ...................................................................... 192 Table A.6. 1. Boltzmann-weighted average descriptors ............................................................. 196 Table A.6. 2. Condensed descriptors .......................................................................................... 198 Table A.7. 1. Comparison of predicted vs. experimentally determined yields of product 2.3 ... 202 Table B.4. 1. HPLC-UV method for purity determination ......................................................... 214 xv List of Figures Figure 1. 1. The key considerations in developing an optimal chemical process. .......................... 2 Figure 1. 2. Optimization algorithm selection based on parameter space definition. ..................... 4 Figure 1. 3. Categories of optimization parameters. ....................................................................... 6 Figure 1. 4. Graphical representation of the dimensions of molecular representation for triphenylphosphine. ......................................................................................................................... 7 Figure 1. 5. MDL Molfile graph of triphenylphosphine. ................................................................ 9 Figure 1. 6. SMILES and InChI identifiers for triphenylphosphine. ............................................ 10 Figure 1. 7. ECFP bits of triphenylphosphine with a radius of 6.................................................. 11 Figure 1. 8. Lowest energy conformer structures and selected quantum mechanical descriptors for triphenylphosphine. ................................................................................................................. 12 Figure 1. 9. Plot of the objective function f(x) = x2, with the optimum marked in red. ............... 14 Figure 1. 10. Five rounds of Bayesian Optimization (BO) with a Gaussian Process (GP) model and Expected Improvement (EI) acquisition function. ................................................................. 16 Figure 1. 11 Illustration of a Pareto front for multiobjective optimization. ................................. 17 Figure 1. 12. Three optimization algorithms in the APO of a Knoevenagel condensation residence time (s) in flow. ............................................................................................................. 19 Figure 1. 13. The APO of a Heck-Matsuda reaction using the Nelder-Mead algorithm: a) cost improvement per experiment, b) Plot of cost with respect to four parameters. ............................ 20 Figure 1. 14. The APO of a Suzuki coupling to maximize TON for the synthesis of 1.9. ........... 21 Figure 1. 15. A reconfigurable system for the APO of various synthetic methodologies. ........... 22 Figure 1. 16. Types of synthetic chemistry automation modules. ................................................ 25 xvi Figure 1. 17. a) Axelsemrau Chronect outfitted with a Mettler-Toledo Auto Chem Quantos solid dispense module b) Chemspeed Technologies GDU-S SWILE with a positive-displacement module........................................................................................................................................... 27 Figure 1. 18. Liquid handling modules arranged in two configurations: A syringe pump, selector valve, and needle tip (top) and a peristaltic pump, 6-port, 2-position valve, and pipette tip (bottom)......................................................................................................................................... 29 Figure 1. 19. Magnetic and mechanical stirring modules impart different agitation homogeneity across a reaction plate, efficiency, and scalability. ....................................................................... 31 Figure 1. 20. Robotic modules for translocation........................................................................... 34 Figure 1. 21. a) HTE to discover optimal conditions for a Suzuki cross-coupling reaction b) HTE to discover optimal conditions for an asymmetric hydrogenation reaction. ................................. 37 Figure 1. 22. HTE in 1536-well batch nanoreactors. .................................................................... 38 Figure 1. 23. Strong base screening workflow involving liquid stock mixture dispensing. ......... 41 Figure 1. 24. Acetoin dimer 1.15 weighing was moved to a glovebox due to hygroscopicity. .... 42 Figure 1. 25. Development of an acidic quench for the optimization of the Vilsmeier Haack bromination of γ-Cyclodextrin. ..................................................................................................... 44 Figure 1. 26. Reaction monitoring with online HPLC analysis enables a more accurate reaction snapshot......................................................................................................................................... 46 Figure 2. 1. Autonomous process optimization platform for the human intervention-free optimization of categorical and continuous process parameters in 96-well batch microreactors with reaction end point analysis. ................................................................................................... 53 Figure 2. 2. The number of publications and patents on metal-catalyzed cross-coupling reactions (pre-1971 through 2010). .............................................................................................................. 54 xvii Figure 2. 3. Reaction monitoring of the stereoselective Suzuki-Miyaura reaction under the catalysis of a variety of palladium complexes to better understand the alkene isomerization mechanism. ................................................................................................................................... 62 Figure 2. 4. Closed-loop system for autonomous optimization. ................................................... 65 Figure 2. 5. Parameters and results of optimization with ligands selected through chemical intuition in campaign 1. ................................................................................................................ 71 Figure 2. 6. K-means clustering on the first four principal components of the molecular descriptor set for 365 commercial monodentate phosphines. ....................................................... 73 Figure 2. 7. Parameters and results of optimization with ligands selected through descriptor clustering in campaign 2. .............................................................................................................. 75 Figure 2. 8. Three-dimensional plot of three continuous parameter selections color-coded for 2.3 yield in campaign 2. ...................................................................................................................... 76 Figure 2. 9. The maximum yield of 2.3 obtained for each monodentate ligand explored in campaigns 1 and 2 mapped onto the chemical space of 365 commercial phosphines. ................ 77 Figure 2. 10. Parameters and results of optimization with ligands selected through descriptor clustering with 15 selected phosphine descriptors included in algorithmic optimization (campaign 3). ................................................................................................................................ 79 Figure 2. 11. Manual experiments under optimized conditions with predicted E-selective ligands........................................................................................................................................................ 80 Figure 2. 12. Two-dimensional plots of 2.3 yield for each experiment ID and average 2.3 yield for each sample bias value in campaign 2. ................................................................................... 82 Figure 2. 13. Random forest (RF) partial dependence plots (PDPs) of optimization parameters on a) 2.3 yield and b) 2.4 yield. ......................................................................................................... 84 xviii Figure 2. 14. Random forest (RF) partial dependence plots (PDP) of a) Phosphine to palladium ratio on 2.3 yield; b) Temperature on 2.3 yield; c) Solvation energy on 2.3 yield. ...................... 86 Figure 2. 15. Random forest (RF) partial dependence plots (PDP) of a) Phosphine to palladium ratio on 2.4 yield; b) Pyramidalization on 2.4 yield; c) Sterimol B5 on 2.4 yield. ....................... 87 Figure 2. 16. Image of the Chemspeed SWING robot deck during autonomous experimentation........................................................................................................................................................ 93 Figure 3. 1. Autonomous process optimization platform for the human intervention-free optimization of categorical and continuous process parameters in 48-well batch photoreactors with reaction time course analysis for dynamic output reporting and flexible optimization strategy implementation. ............................................................................................................. 101 Figure 3. 2. Spectroscopy studies in the presence and absence of citric acid. ............................ 106 Figure 3. 3. Five rounds of high-throughput reaction profiling to identify the ideal reagent, additive, additive loading, temperature and solvent for a radical benzylic bromination. ........... 108 Figure 3. 4. Results with NBS in the first round of automated high-throughput reaction profiling for reagents and additives. .......................................................................................................... 109 Figure 3. 5. Results with NBS in the second round of automated high-throughput reaction profiling for solvents. .................................................................................................................. 110 Figure 3. 6. Results in ACN in the third round of automated high-throughput reaction profiling for additive loadings. .................................................................................................................. 111 Figure 3. 7. Results of the fourth round of automated high-throughput reaction profiling for temperature. ................................................................................................................................ 112 Figure 3. 8. Scale-up experiments in a Vaportec photoreactor. .................................................. 113 xix Figure 3. 9. Results of the fifth round of automated high-throughput reaction profiling for water-immiscible solvents. .................................................................................................................... 115 Figure 3. 10. Reaction monitoring at 15 oC under light-dark cycling and constant light. .......... 117 Figure 3. 11. Reaction monitoring at 35 oC under constant light. .............................................. 118 Figure 3. 12. NMR studies of NBS with the addition of various reaction species in solution. .. 119 Figure 3. 13. Components of a Chemspeed SWING XL system for high-throughput autonomous photochemistry optimization with time course analysis. ............................................................ 120 Figure 3. 14. Temperature monitoring of the Lumidox II LED panel with the Paradox photoreactor. ............................................................................................................................... 122 Figure 3. 15. Temperature monitoring of the Lumidox TCR photoreactor. ............................... 123 Figure 3. 16. Evaluation of the plateau variability algorithm on data from the first through fifth rounds of automated high-throughput reaction profiling. ........................................................... 126 Figure 3. 17. Steps in a sequential model based optimization workflow. .................................. 127 Figure 3. 18. Data flow in the integrated closed-loop optimization system. .............................. 130 Figure 3. 19. Time course monitoring of LC area % product 3.2 for reproducibility testing. .... 134 Figure 3. 20. Reaction profiles of the first campaign with a linear regression model (LM) based optimization sampled with the predicted mean acquisition function.......................................... 137 Figure 3. 21. Reaction profiles of the second campaign with Gaussian process (GP) model-based Bayesian optimization (BO) sampled with the expected improvement (EI) acquisition function...................................................................................................................................................... 138 Figure 3. 22. Reaction profiles of the third campaign with Gaussian process (GP) model-based Bayesian optimization (BO) sampled with alternating expected improvement (EI), probability of improvement (PI), and upper confidence bound (UCB) acquisition functions. ......................... 139 xx Figure 3. 23. Visualization of the first campaign with a linear regression model (LM) based optimization sampled with the predicted mean acquisition function.......................................... 141 Figure 3. 24. Visualization of the second campaign with Gaussian process (GP) model based Bayesian optimization (BO) sampled with the expected improvement (EI) acquisition function...................................................................................................................................................... 143 Figure 3. 25. Visualization of the third campaign with Gaussian process (GP) model-based Bayesian optimization (BO) sampled with alternating expected improvement (EI), probability of improvement (PI), and upper confidence bound (UCB) acquisition functions. ......................... 144 Figure 3. 26. Visualization of the results from three combined optimization campaigns. This gives the most comprehensive view of the process global optimum, the various local optima and process insights around additive loading. ................................................................................... 146 Figure 3. 27. Box plot of LC area % product under various sampling strategies. ...................... 148 Figure 3. 28. Count of iterations per each additive under different sampling strategies. ........... 149 Figure 3. 29. Multidimensional scaling (MDS) visualizations. .................................................. 150 Figure 3. 30. Random forest (RF) model of the combined autonomous optimization data. ...... 152 Figure 3. 31. Image of the Chemspeed SWING XL deck for autonomous experimentation. .... 158 Figure 4. 1. The APO approach can be extended to a variety of automation equipment ........... 162 Figure A.1. 1. Definitions of angles used to calculate pyramidalization. Pyramidalization can be calculated for any tricoordinate atom. ........................................................................................ 183 Figure A.1. 2. Definition of Sterimol features. ........................................................................... 183 Figure A.3. 1. MS ESI+ of 2.1. ................................................................................................... 184 Figure A.3. 2. MS ESI+ of 2.3. ................................................................................................... 184 Figure A.3. 3. MS ESI+ of 2.4. ................................................................................................... 184 xxi Figure A.3. 4. 1H and 13C NMR spectra of 2.1 in DMSO-d6. ..................................................... 185 Figure A.3. 5. 1H and 13C NMR spectra of 2.3 in DMSO-d6. ..................................................... 186 Figure A.3. 6. 1H and 13C NMR spectra of 2.4 in DMSO-d6. ..................................................... 187 Figure A.3. 7. 1H and 31P NMR spectra of L20 in CDCl3. ......................................................... 188 Figure A.5. 1. Calibration curves for conversion of compound (2.1, 2.3, 2.4) to internal standard (IS) HPLC area ratios at 210 nm to mol % distribution in the reaction mixture. ....................... 193 Figure A.6. 1. Chemspeed protocol for autonomous optimization of the Suzuki reaction......... 195 Figure B.1. 1. Results with DBDMH in the first round of automated high-throughput reaction profiling studies. ......................................................................................................................... 203 Figure B.1. 2. Results in AcOH in the third round of automated high-throughput reaction profiling....................................................................................................................................... 204 Figure B.1. 3. Plateau detection test with algorithm 1. ............................................................... 205 Figure B.1. 4. Plateau detection test with algorithm 2. ............................................................... 206 Figure B.1. 5. Plateau detection test with algorithm 3. ............................................................... 207 Figure B.1. 6. Gaussian process (GP) model for three consecutive optimization iterations in campaign 3. ................................................................................................................................. 208 Figure B.1. 7. Simulated optimization runs based on a neural network model of the combined data. ............................................................................................................................................. 209 Figure B.3. 1. MS ESI+ of 3.1. ................................................................................................... 210 Figure B.3. 2. MS ESI+ of 3.2. ................................................................................................... 210 Figure B.3. 3. MS ESI+ of 3.3. ................................................................................................... 210 Figure B.3. 4. 1H and 13C NMR spectra of 3.1 in DMSO-d6. ..................................................... 211 Figure B.3. 5. 1H and 13C NMR spectra of 3.2 in DMSO-d6. ..................................................... 212 xxii Figure B.3. 6. 1H and 13C NMR spectra of 3.3 in DMSO-d6. ..................................................... 213 Figure B.5. 1. Chemspeed protocol for autonomous optimization of the Wohl-Ziegler reaction..................................................................................................................................................... 217 xxiii List of Schemes Scheme 2. 1. Phosphine ligand influence on a stereoselective Suzuki-Miyaura coupling to generate the stereoinversion product (2.4) or stereoretention product (2.3). ................................ 52 Scheme 2. 2. Alkenyl sulfonates as viable partners in Suzuki cross-couplings. .......................... 55 Scheme 2. 3. Proposed mechanism of the cross-coupling of alkenyl sulfonate pseudo halides or halides with aryl boronic acids or boronate esters. ....................................................................... 56 Scheme 2. 4. First report of stereoinversion in a Suzuki coupling.140 .......................................... 57 Scheme 2. 5. Reports of phosphine ligand influence on stereoinversion under palladium catalysis in Negishi, Suzuki and Stille couplings.149–151 .............................................................................. 57 Scheme 2. 6. Proposed mechanisms of stereoinversion in palladium-catalyzed couplings ......... 59 Scheme 2. 7. Proposed mechanism for stereoinversion in the Suzuki couplings of haloenones.153....................................................................................................................................................... 60 Scheme 2. 8. Standard experimental conditions for reproducibility testing. ................................ 68 Scheme 3. 1. Radical benzylic halogenation reaction identified for autonomous optimization. 102 Scheme 3. 2. The general reaction scheme of a Wohl-Ziegler bromination reaction. ................ 103 Scheme 3. 3. Mechanism of the Wohl-Ziegler photobromination reaction. ............................... 104 Scheme 3. 4. Multi gram-scale demonstration of a benzylic photobromination in flow.196 ....... 105 Scheme 3. 5. Multi kilogram-scale demonstration of a benzylic photobromination in flow.184 . 106 Scheme 3. 6. Reaction conditions for reproducibility testing. .................................................... 133 Scheme 3. 7. Photobromination reaction and process parameters under study. ......................... 140 Scheme A.7. 1. Selected ligands. ................................................................................................ 201 Scheme A.7. 2. Compounds related to selected ligands. ............................................................ 202 xxiv List of Abbreviations 0D Zero dimensional 1D One dimensional 2D Two dimensional 3D Three dimensional 4D Four dimensional ACN Acetonitrile AI Artificial intelligence AIBN Azobisisobutyronitrile API Application programming interface APO Autonomous process optimization ASF Achievement scalarizing function AY Assay yield BO Bayesian optimization CV Coefficient of variance DBDMH 1,3-dibromo-5,5-dimethylhydantoin DCDMH 1,3-dichloro-5,5-dimethylhydantoin DFT Density-functional theory DMC Dimethylcarbonate DoE Design of experiments ECFP Extended-connectivity fingerprints xxv EI Expected improvement FTIR Fourier-transform infrared spectroscopy GC Gas chromatography GP Gaussian process HOMO Highest occupied molecular orbital HPLC High-performance liquid chromatography HTE High-throughput experimentation InChI IUPAC chemical identifier IUPAC International union of physical and applied chemists LC Liquid chromatography LCB Lower confidence bound LED Light-emitting diode LM Linear regression model LUMO Lowest unoccupied molecular orbital MDS Multidimensional scaling MISER Multiple injections in a single experimental run ML Machine learning MS Mass spectrometry NBS N-bromosuccinimide NCS N-chlorosuccinimide NMR Nuclear magnetic resonance spectroscopy OVAT One variable at a time xxvi PCA Principal component analysis Pd Palladium PI Probability of improvement PPA Phenylphosphonic acid QM Quantum mechanics RF Random forest RSD Relative standard deviation SD Standard deviation SHE Safety, health and environmental SMBO Sequential model-based optimization SMILES Simplified molecular-input line-entry system SNOBFIT Stable noisy optimization by branch and fit TCR Temperature-controlled reactor TON Catalyst turnover number UCB Upper confidence bound UMAP Uniform manifold approximation and projection UPLC Ultra-performance liquid chromatography UV-Vis Ultraviolet-visible spectroscopy xxvii Acknowledgements I would like to start by thanking my supervisor Jason E. Hein, who has supported every facet of my development over the past four years. Thank you for your support, your flexibility and for sharing your precious wisdom. Thank you for promoting risk-taking and letting me fail, so that I could learn from my mistakes. Thank you for your kindness. Thank you for your enthusiasm for science and technology. It is a true joy to learn from you. I would also like to thank my graduate committee for taking the time to guide and review my research and progress. I would like to thank everyone in the Hein Group, especially Dr. Lars Yunker and Dr. Tara Zepel. Without your help, this autonomous optimization work would remain a dream. I would also like to thank Shad Grunert, Paloma Prieto, Parisa Shiri, Sean Clark and Luke Yang for their automation and coding support. I would like to thank my study buddies Andrew Kukor, Yusuke Sato, Junliang Liu, Tristan Maschmeyer, Maddie Deem and Dr. Josh Derasp for banding together during the pandemic in preparation for our candidacy exams. Even though I was far away in New Jersey, I always felt like I was part of the group. I would like to thank Dr. Rebecca Ruck, Dr. John Naber, Dr. John Jurica, Dr. Alex Sun and Dr. Shane Grosser at MSD Process Research for allowing me to carve out the space to complete this research, even when that meant taking on more work for them. I owe a special thanks to Dr. Eugene Kwan, Dr. Yuting Xu and Dr. Andy Liaw for their contributions on the coding and data science side, as well as for taking the time to teach me these exciting new tools. You really helped me get where I needed to be. Many thanks to Dr. Yining Ji Chen for being my friend, my office mate and xxviii my mechanism guru. You have helped me grow in countless ways. I also owe thanks to Folarin Adedeji, I hope we get to work together again one day. Finally, thanks to Dr. Mike DiMaso, Dr. Reed Larson and Dr. Patrick Fier for taking a chance on a new automation technology. I would like to thank Prof Alán Aspuru-Guzik, Dr. Florian Häse, Dr. Loïc Roch and Dr. Gabriel dos Passos Gomes from the University of Toronto for their collaboration and support. I would also like to thank Prof Matthew S. Sigman and Dr. Tobias Gensch from the University of Utah for their collaboration and support. You are pioneers in cheminformatics and data science, and I was fortunate enough to have had the opportunity to work with you. I’d like to thank the funding agencies, the University of British Columbia and MSD Process Research for allowing me to pursue this PhD and carry out the research remotely from the MSD Process Research labs in Rahway, New Jersey. I’d like to thank my family. My husband John has been my biggest supporter from the moment I’ve met him and he has made my home a safe, happy and cozy refuge. My mother Selma and father Ron, thank you for raising me and working hard to put me through school so that I could pursue science. My sister Melissa, my brother-in-law Carl and my nephews, Alex and Nico for being my happy place. My grandparents, wish you were alive today. Especially Dede, thank you for the immeasurable wisdom you shared with me. I always think “What would Dede do?” And finally, my friends, you are my chosen family. Thank you for your support, encouragement, openness and for all the good times. I love you all so much. We are lucky to have found each other. xxix Dedication For Becky – your confidence in me helped me realize my true potential. xxx “The obstacle is the path.” — Zen Proverb 1 Chapter 1: Introduction 1.1 Introductory remarks I have been a working professional in the chemical process development field for 17 years. During this time, the field has evolved from applying the fundamentals of process optimization in manual, single experimental configurations to automated, high-throughput configurations. I, along with others in the field, have attempted to position ourselves at the forefront of this technology adoption curve in order to improve the efficiency, quality, depth and impact of our work. This journey has evolved further in my PhD research through merging automation and artificial intelligence (AI) to develop autonomous, high-throughput chemical process development platforms. Chemical process development involves the design of synthetic routes with the ultimate goal of manufacturing chemicals on an industrial scale. Similar to lab scale synthetic route development, industrial scale synthetic route development entails the consideration of synthetic efficiency, but the concerns do not end there. The optimal chemical process is based on additional considerations such as robustness, cost, operational ease, safety, environmental impact, and the quality of the synthesized material (Figure 1.1).4 Robustness refers to the ability of a chemical process to tolerate the expected variability of raw materials, operating conditions, process equipment, environmental conditions, and human factors.5 Thus, chemical process development is a multifaceted problem that requires careful planning. Typically, multiple synthetic approaches are evaluated and experimentally fine-tuned, or optimized, until the ideal process is identified. Experimental optimization is repetitive, but requires a high level of control and detailed observation. The repetitive and controlled nature of experimental optimization has led to the natural evolution of 2 high-throughput automation technologies for its implementation, but the missing link has always been the detailed observation component.6–8 The introduction of artificial intelligence (AI) tools can incorporate the missing link of detailed observation into automated experimental workflows. Figure 1. 1. The key considerations in developing an optimal chemical process. This idea led to my PhD work involving the development of autonomous systems for chemical process optimization through the integration of commercial lab automation platforms with online analytics for experimental evaluation and optimization algorithms for experimental planning. I carried out this work in the MSD Process Research and Development labs in Rahway, NJ, under the remote guidance of Prof Jason Hein. I was able to convert two high-throughput automation platforms belonging to MSD into high-throughput autonomous platforms for chemical process optimization. I was also successful in demonstrating their effectiveness in the self-optimization of two chemical reactions of interest to the pharmaceutical industry. These multidisciplinary projects were developed in collaboration with multiple academic and industrial data scientists and coders, 3 and the platform design, integration concept, experimental execution and data analysis were carried out my myself. In my experience, this collaborative work model between experimentalists and data scientists worked exceedingly well in the development of this particular AI-driven chemistry technology and can serve as a model for future activities of this nature. 1.2 Overview Artificial intelligence (AI) has several definitions, but perhaps the most comprehensive definition was invoked by the social scientist Marvin Minsky as “the science of making machines do things that would require intelligence if done by humans.” The field of AI is wide-ranging, encompassing subfields such as machine learning, computer vision, robotics, autonomous systems and natural language processing.9 Autonomous process optimization (APO) is the human intervention free self-optimization of input process parameters such as catalyst and temperature in order to minimize or maximize process output parameters such as yield and selectivity.10–15 APO involves definition of the search space, in other words, the selection of input and output process parameters, as well as the “closed-loop” integration of an optimization algorithm with an automated experimental execution platform such as a liquid handling robot with an online analytical instrument such as a liquid chromatography instrument with UV detection. The work described here focuses on APO in the chemical sciences. Section 1.1 commences with a discussion of input and output process parameter selection. Since a large portion of process parameters in the chemical sciences are associated with chemical structure, a particular emphasis will be placed on the machine-readable representation of chemical structure. 4 The section describes suitable optimization algorithm selection based on the parameter space. Finally, the section closes with a literature survey of APO in flow and batch chemistry settings. Section 1.2 introduces automated batch chemistry as a strong candidate for the experimental execution of APO.16 Under careful execution, automated high-throughput batch chemistry can generate uniform, high-quality data sets ideal for algorithmic optimization. The section describes the meaning of “careful execution” from equipment, experimental and data perspectives. 1.3 Autonomous chemical process optimization The assembly of input parameters, optimization algorithms and output parameters can be likened to the fitting together of puzzle pieces (Figure 1.2). This is because each optimization algorithm is designed based on certain assumptions around the parameter space, in other words, each algorithm is best suited to a certain optimization scenario.17,18 Early reports of APO in chemical process development were limited to simple parameter space evaluations,19,20 but the evolution of optimization algorithms has opened up the possibility of multidimensional parameter space evaluations.21–31 Thus, examples of multidimensional APO have begun to permeate the field. Figure 1. 2. Optimization algorithm selection based on parameter space definition. 5 1.3.1 Input parameters Input parameter selection in APO is an important feature that requires careful thought and consideration. The input parameter space will, in part, define the algorithms available for optimization. There are multiple strategies that can be implemented in categorical input parameter representation, which, in the field of chemical sciences, typically translates to chemical structure representation. Chemical structure representation can have significant downstream effects on optimization performance; therefore, this topic will be discussed in detail.32 1.3.1.1 Continuous, categorical and discrete parameters There are two major parameter categories to consider in algorithmic optimization: numerical and categorical (Figure 1.3). Numerical parameters are further divided into discrete and continuous, where discrete parameters refer to counted numbers that are distinct and separate (e.g. light intensity settings between 1 and 5), while continuous parameters refer to measured numbers that can take on any value within an interval (e.g. stoichiometry, temperature, pKa). Categorical parameters are further divided into nominal and ordinal, where nominal parameters refer to categories with no ordering or direction (e.g. reagent, solvent, catalyst), while ordinal parameters refer to categories with rankings, order or scaling (e.g. low, medium or high yield). Categorical parameters pose a unique challenge to optimization algorithm selection because a majority of algorithms are designed to optimize numerical parameters.33 Chemical parameters that fall into the nominal category and are often difficult to represent in algorithmic optimization and thus warrant further discussion.34,35 6 Figure 1. 3. Categories of optimization parameters. 1.3.1.2 Chemical parameter representation Process optimization in the chemical sciences involves the consideration of a number of chemical parameters including, but not limited to, reagents, catalysts, ligands, additives and solvents. In the context of algorithmic optimization, the scientist is presented with a choice around whether or not to incorporate molecular representations of these chemical parameters in the optimization or parameter selection process. Sometimes the answer is to leave out molecular representation completely, while in other instances incorporation of molecular representation can improve the optimization performance.33 As the investigator moves from non-molecular representation to the inclusion of higher dimensions of molecular features, the required computational resources increase significantly.32,36 Thus, the balance between computational resources and the improvement of optimization performance 7 through increased structural detail must be weighed carefully for each optimization challenge. Molecular representation can vary from zero- to four-dimensional (Figure 1.4).37 Figure 1. 4. Graphical representation of the dimensions of molecular representation for triphenylphosphine. 1.3.1.2.1 Non-molecular representation The simplest representation of a chemical parameter is to omit molecular representation. Here, each categorical parameter is treated individually by the algorithm. Another choice is to map chemical parameters to integers (ordinal encoding) or binary vectors (one hot encoding).38 One hot encoding is a technique utilized commonly in the machine learning field for transforming categorical parameters to continuous parameters through the generation of binary vectors. Consider three organic solvent options for an optimization, including acetonitrile, tetrahydrofuran and toluene. Integer encoding transforms each solvent to an integer value such as 1, 2 and 3. One hot encoding transforms each solvent to binary vectors such as [1,0,0], [0,1,0] and [0,0,1]. Such 8 representations allow for the use of a wider selection of optimization algorithms, but lack structural information. 1.3.1.2.2 Molecular representation The simplest form of molecular representation provides details around the atomic make-up of a molecule, but no information around the connectivity between atoms. The molecular formula of triphenylphosphine is an example (C18H15P).34 The molecular formula conveys the number of carbon, hydrogen and phosphorus atoms in triphenylphosphine, but does not provide information around how these atoms are ordered or connected. Molecular weight is also a relevant example. 1D molecular representation typically involves the binary coding of functional groups in a molecule, but lacks information around the connectivity between these functional groups. Thus, a one-dimensional representation of triphenylphosphine would confirm the presence of phenyl moieties in the molecule, but provide no information regarding their connectivity.37 2D molecular representations convey both atom make-up and connectivity information between the atoms of a molecule. Molecular graphs are representations of molecular structure based on graph theory. A chemical graph is a labeled graph whose vertices correspond to the atoms of the compound and edges correspond to chemical bonds. For example an MDL Molfile is a file format for holding information about the atoms, bonds, connectivity and 2D coordinates of a molecule (Figure 1.5).39 In an MDL Molfile, the first block is the atom block, with one atom represented by each line, and the second block is the bond block, with one bond represented by each line. 9 Figure 1. 5. MDL Molfile graph of triphenylphosphine. Generated through RDkit.40 10 Molecular graphs require a fair amount of storage space, therefore line notations such as Simplified Molecular-Input Line-Entry System (SMILES)41–43 and the IUPAC Chemical Identifier (InChI),44–46 that represent structures as a linear string of characters, can be a more effective method for holding information about the atoms, bonds and connectivity of a molecule while imposing lower computational burden (Figure 1.6). Figure 1. 6. SMILES and InChI identifiers for triphenylphosphine. Molecular fingerprints are a method generating machine learning-friendly binary representations of molecular structure. Extended-Connectivity Fingerprints (ECFP) are particularly data rich circular fingerprints, based on the Morgan algorithm, that have been widely adopted in the machine learning field. ECFPs account for the connectivity between each atom and its neighbors, and their computation involves a three-stage process. The initial assignment stage involves integer identifier assignment to each atom. This is followed by the iterative updating stage in which each atom identifier is updated to reflect the identifier of each atom’s neighbors, through iterative radius expansion. The final step involves duplicate identifier removal and generation of the bit vector (Figure 1.7).47 11 Figure 1. 7. ECFP bits of triphenylphosphine with a radius of 6. Generated through RDkit.40 3D and 4D molecular representations are computed features that provide information around the three dimensional structure of a molecule. This requires the lowest energy conformer (3D) or collection of conformers (4D) for a molecule to be calculated.34,37 The 3D coordinates of conformers can be stored as atomic coordinate files. Quantum mechanical (QM) descriptors involve HF Ab Initio or DFT calculations of electronic and steric features such as LUMO/ HOMO energies, atomic charges, Fukui coefficients,48 Tolman cone angle,49 buried volume50 and Sterimol51 (Figure 1.8). Quantum mechanical descriptor calculations require significant computational resources and this method need only be applied when it is essential to optimization performance and exploratory analysis.37 A significant advantage of 3D molecular features is the ability to draw correlations between process performance, steric and electronic features, leading to mechanistic insights around the system under study.52–54 12 Descriptor Brief Description Boltzmann* vmin Minimum molecular electrostatic potential in P lone pair -0.048 nbo_P Phosphorus natural population (NBO) 0.798 nmr_P Isotropic chemical shielding constant P 296.153 nbo_lp_P_e P lone pair energy (NBO) -0.308 nbo_bd_e_avg P-C bonding orbital energy, average (NBO) -0.477 E_solv_total Energy of solvation -13.921 dipolemoment Dipole moment 1.461 qpole_amp Quadrupole moment amplitude 4.561 pyr_P Pyramidalization 0.932 vbur_vbur Buried volume 50.689 vbur_qvbur_min buried quadrant volume, lowest 11.489 vbur_qvbur_max buried quadrant volume, highest 13.884 sterimol_B1 Sterimol B1 4.288 sterimol_B5 Sterimol B5 6.324 sterimol_L Sterimol L 7.182 Figure 1. 8. Lowest energy conformer structures and selected quantum mechanical descriptors for triphenylphosphine. Obtained from the Kraken database.55 *Boltzmann-weighted conformer average 13 1.3.1.2.3 Chemical intuition based categorical parameter selection Molecular representation is not only an important factor to consider with respect to categorical parameter representation in the optimization, but also with respect to categorical parameter selection.38 Categorical parameter selection for process optimization is typically rooted in chemical intuition, which can be defined as a combination of literature precedent and subject matter expertise. This approach may not lead to an equally distributed representation of the categorical parameter chemical space, leaving stones unturned and molecular features underexplored. Despite its shortcomings, chemical intuition based categorical parameter selection dominates in the field of process development. A more systematic approach is machine learning based selection, and this approach is discussed in the following subsection.53,56 1.3.1.2.4 Machine learning based categorical parameter selection Machine learning techniques such as dimensionality reduction and clustering are invaluable tools for categorical parameter selection based on computed structural features. Dimensionality reduction techniques such as principal component analysis (PCA) and uniform manifold approximation and projection (UMAP)57 allow for the dimensionality reduction of large, computed molecular feature sets.55,58,59 Clustering techniques such as K-means58,60 or hierarchical53 clustering allow for the division of the categorical parameter space into sections based on similarity, allowing for systematic sampling of the parameter space. Both dimensionality reduction and clustering are powerful machine learning techniques that allow for a more diverse representation of the available chemical process parameters. 14 1.3.2 Optimization Algorithms Optimization algorithms determine the input parameters that result in the optimal output parameters for an objective function, f(x). The visualization provided in Figure 1.9 plots a simple, well-defined, one-dimensional objective function, f(x) = x2, with the optimum value highlighted at x = 0.61,62 Optimizations in the chemical sciences field are seldomly this simple, in fact, the objective function is typically not known, requiring a surrogate model of the objective function to be optimized.17 Figure 1. 9. Plot of the objective function f(x) = x2, with the optimum marked in red. Grid search involves the full factorial sampling of a grid of evenly spaced points over the objective function. Random search, on the other hand, lacks structure and involves the random sampling of points over the objective function. Grid search evenly covers the search space, but involves a large number of points.61 Gradient based optimizations use a gradient of the objective function to guide sampling of the search space. This method can be updated to use an automatically adaptive step size for each input parameter. Gradient free optimizations, as the name implies, do not use a gradient of the objective function to guide sampling of the search space.61 In the field of chemical process optimization, the Nelder-Mead (or Simplex) method19,24,28 is often utilized, which uses a geometric shape, termed a simplex, to guide the sampling of the search space. For n dimensions, 15 the simplex will have n + 1 vertices. The optimization begins with a randomly generated simplex, and at every iteration, the simplex is reshaped and moved one vertex at a time until the optimal region is reached. The simple optimization algorithms described up to this point rely on a single sample of the search space to initiate the optimization. Population based algorithms differ from this approach in that a population of multiple initial samples are utilized initiate the optimization.61 Common examples are the genetic algorithm63,64 and particle swarm optimization. In chemical process optimization, mapping out the objective function for any given search space would require the experimental sampling of every combination of every input parameter permutation. In other words, chemical process objective functions are “expensive” to sample. In these cases, it is best to rely upon a model of the objective function, often termed a surrogate model. As additional samples are evaluated with each iteration, the model is sequentially refined to make better predictions. Hence, this approach to optimization has been termed sequential model based optimization (SMBO).65 Common examples in the chemical sciences include Stable Noisy Optimization by Branch and Fit (SNOBFIT),10,66 which relies on linear and quadratic models, and Bayesian Optimization (BO),67–72 which commonly relies on Gaussian Process (GP) regression or Random Forest (RF) models. In sequential model based optimization, both the surrogate model and variance (also coined the uncertainty estimate in GP modeling) play an important role in the selection of samples for sequential evaluation. However, there is another function that must be utilized to define the selection criteria, termed the acquisition function. Some acquisition functions are geared towards the selection of optimal points, such as probability of improvement (PI) and expected improvement (EI), while other acquisition functions sample areas of high variance, such as upper confidence bound (UCB) or lower confidence bound (LCB), where less frequent sampling 16 has previously occurred.67,68 The acquisition function significantly impacts the optimization performance and potential to arrive at a global optimum. Figure 1.10 illustrates four rounds of BO through GP regression with an EI acquisition function. Surrogate GP model (µGP(x)) Acquisition function (EI(x)) Figure 1. 10. Five rounds of Bayesian Optimization (BO) with a Gaussian Process (GP) model and Expected Improvement (EI) acquisition function. Generated through Scikit-optimize.73 17 1.3.3 Output Parameters Output parameters in chemical process optimization typically include synthetic outcomes such as yield, selectivity or catalyst turnover number. The truth of the matter is that although outputs related to synthetic efficiency are important, additional outputs of importance in process chemistry include impurity control, sustainability, efficiency, robustness, cost, safety and ease of operation.74 Thus, a more realistic evaluation of a chemical process would consider multiple objectives simultaneously. Single objective optimizations are straightforward in that they require the optimization of one output parameter such as yield. Multiobjective optimizations, on the other hand, require the formulation of an optimal solution that is based on tolerated trade-offs among the objectives.61,75 The Pareto front, the set of all Pareto optimal solutions, is an effective visualization commonly used to assess these trade-offs (Figure 1.11). Figure 1. 11 Illustration of a Pareto front for multiobjective optimization. The pink circles represent the Pareto optimal solutions, the teal curve the Pareto front, and the grey circles the non-optimal solutions. In an autonomous optimization scenario, the preferred Pareto optimal solution selection process must be automated. A common approach for determining the preferred Pareto optimal solution is 18 the construction of an achievement scalarizing function (ASF) that combines multiple objective functions into a single objective function with pre-determined weights associated with each term. A second approach, known as the ɛ-constraint approach, involves considering only one objective while constraining the other objectives based on user preferences. A third approach, known as the lexicographic approach, requires the definition of a hierarchy of objectives and tolerances based on user preferences. Each objective is then subsequently optimized without degrading higher level objectives.76 1.3.4 APO in Flow Chemistry As mentioned in the early introduction, several examples of APO to date have been implemented in flow chemistry settings.10–20 The reason behind this appears to be the ease of online analytical instrument integration and automated process parameter control (temperature, flow rate and residence time). The following section outlines seminal examples of the implementation of APO in flow chemistry settings. One of the earliest demonstrations of APO in a continuous flow chemistry setting involves the comparison of three optimization algorithms in the optimization of a Knoevenagel condensation reaction.19 In this example, Jensen and coworkers compare the Nelder-Mead, Steepest Descent using Design of Experiment (DoE) and Stable Noisy Optimization by Branch and Fit (SNOBFIT) methods in the optimization of two continuous parameters, reaction temperature and residence time, to maximize a weighted multiobjective function (Figure 1.12). 19 Figure 1. 12. Three optimization algorithms in the APO of a Knoevenagel condensation residence time (s) in flow. High objective function values (good) are indicated by red color. Reprinted with permission from reference 19.19 Copyright 2010 American Chemical Society. 20 A second demonstration of APO in a continuous flow chemistry setting involves the implementation of the Nelder Mead method in the optimization of a Heck-Matsuda reaction.24 Here, Felpin and coworkers optimize the yield of compound 1.6 (%), throughput (mg/h) and cost (€/g) by varying the continuous input parameters of temperature, residence time, diazonium salt equivalents and catalyst loading. Instead of combining the output parameters into one multiobjective optimization, they carry out a single objective optimization for each output parameter. Here we show the optimization for cost minimization as an example (Figure 1.13). Figure 1. 13. The APO of a Heck-Matsuda reaction using the Nelder-Mead algorithm: a) cost improvement per experiment, b) Plot of cost with respect to four parameters. Reprinted with permission from reference 23.24 Copyright 2016 American Chemical Society. 21 Multiparameter optimizations involving combinations of categorical and continuous parameters pose a unique challenge in continuous flow chemistry settings because of the time it would take to reach steady-state conditions for each categorical process parameter. This problem has been solved through the implementation of segmented flow (droplet microfluidic) systems, where each reaction droplet contains a single set of parameter point selections, and each droplet is separated by an inert carrier phase. This eliminates the need for steady-state equilibration. Buchwald and Jensen implemented a segmented flow reactor system in the APO of Suzuki cross-coupling reactions through the selection of a categorical parameter from eight palladium precatalyst choices in combination with three continuous parameters.22 APO was carried out on four cross-couplings between various heteroaryl chlorides and heteroaryl boronates. The output parameter was catalyst turnover number (TON) and the optimization algorithm was a Design of Experiment (DoE) based algorithm. The optimization trajectory for compound 1.9 is highlighted below (Figure 1.14). Figure 1. 14. The APO of a Suzuki coupling to maximize TON for the synthesis of 1.9. Reproduced from reference 22 with permission from the Royal Society of Chemistry.22 22 A majority of APO reports in flow chemistry focus on a single chemical system. However, a recent report by Jensen and Jamison demonstrates the possibility of a flexible system capable of the APO of various chemical systems (Figure 1.15).23 In this report, a reconfigurable continuous flow system was implemented in the APO of six reactions, including a Horner-Wadsworth-Emmons (HWE) olefination, a reductive amination, a Suzuki-Miyaura cross-coupling, a nucleophilic aromatic substitution (SNAr), an oxidative photoredox-catalyzed cyanation, and a 2+2 cycloaddition. The optimizer of choice was the SNOBFIT algorithm, which appeared to facilitate the optimization of a wide variety of continuous parameters. High-yielding conditions were discovered in each example, and the substrate scope under the optimal conditions proved to be general. The downside of this system was its inability to optimize categorical parameters. Figure 1. 15. A reconfigurable system for the APO of various synthetic methodologies. From reference 31.23 Reprinted with permission from AAAS. 23 1.3.5 APO in Batch Chemistry The discussion of APO in flow chemistry systems highlights the utility of this novel technology, but also reveals the need for flexible systems capable of categorical and continuous parameter optimizations in tandem. Although this is possible in segmented flow systems, microfluidic systems are limited to sequential sampling. It would be highly enabling to carry out tandem categorical and continuous parameter optimizations in higher throughput, where evaluations could be batched to drive cycle time reduction. Automated high-throughput batch microreactors, which are typically designed in 96-well format, are well suited for this purpose. High-throughput batch micoreactors have the additional advantage of accommodating solid-liquid phase heterogenous reactions. Surprisingly, we have not seen any examples of high-throughput APO to date, however, the potential value of its implementation has been recognized by experts in the field.16 Thankfully, high-throughput automation has seen increasing implementation in the chemical sciences,77 and the next section will detail the key considerations around its implementation. 1.4 Batch Chemistry Automation in the Chemical Sciences Synthesis is often characterized as much as an art as it is a science.78,79 Successful chemical experimentation requires the careful execution of a highly variable series of complex reactions and steps, not to mention interaction with a wide range of equipment. Deciding what the next step in a particular workflow should be is often based on experience and chemists’ intuition. 24 The appeal of automation often lies in the potential for increased efficiency (by offloading repetitive tasks and increasing throughput), reproducibility (given the high precision of robotic tools), and safety (where harmful chemicals or reactions can be performed with reduced human exposure). Importantly, automation opens up capabilities that are difficult to carry out by humans in a practical manner.77 The problem is that the why is only a small part of the automation process. What remains far less discussed in the literature outside the implementation of specific systems is the question of how. How does one go about automating chemical processes in general? This section takes the premise that automating chemical processes is challenging as a starting point and systematically walks through key considerations and decisions that must be made. Automation isn’t automatic. Rather, it is an iterative process that involves careful interrogation and continuous adjustment on the part of both humans and machines. Different chemical systems and experimental parameters often fundamentally change what is required of an automated system. Therefore, even small considerations when getting started, such as whether a pump can accommodate different phase states, are nontrivial. The following section critically examines the key factors and decisions required for the successful automation of chemical experimentation. The topics are divided into three categories: (1) Equipment considerations, (2) Experimental considerations, and (3) Data and Software. 1.4.1 Equipment Considerations What experimental tasks are to be automated? For example, setting up and analyzing an organic reaction typically involves solid dispensing, liquid dispensing, stirring and temperature control. Modules to perform each of these unit operations can be built or purchased. Even if the modules 25 are purchased, it is essential that the researcher understands their inner workings in order to effectively utilize and troubleshoot the equipment. Provided is a high-level overview of the common modules utilized in chemistry automation (Figure 1.16) and their hardware components. Figure 1. 16. Types of synthetic chemistry automation modules. Figure by Tara Zepel. 1.4.1.1 Solid and liquid handling Solid dispensing is ubiquitous in synthetic chemistry, with scientists spending hours at the scale, performing manual weighing tasks. While desirable, automating this task is challenging due to the wide range of properties that solids can exhibit, from density to particle size distribution and flowability. Currently, there are two main types of solid dispensing modules utilized in chemistry automation and both rely on gravimetric dispensing: the first type involves hopper/ feeder modules and second involves positive displacement modules.80,81 26 Hopper/ feeder modules are gravimetric solid dispense tools that consist of a hopper to which the solid is loaded with an opening at the bottom. Various types of feeders guide the solid flow through to the bottom port. Typically, the port opening is controlled through a rotary valve and the feeder action controlled through various mechanical means. In some instances, the solid flow can be additionally controlled through tapping or vibrational actions. The Mettler-Toledo Auto Chem Quantos utilizes a hopper-based module where the flow of solids is controlled through rotary tapping (Figure 1.17a).82 Hopper/ feeder modules are best suited for milligram to gram quantity solid dispensing. In addition, hopper/ feeder modules are best suited to dispense free-flowing solids, as interruptions in solid flow may lead to a device timeout. Positive displacement modules are also gravimetric, but rely on capillaries outfitted with pistons which move up and down to pick up and dispense solids through positive displacement. The Chemspeed Technologies GDU-S SWILE is an example of a positive-displacement module (Figure 1.17b).83 The SWILE in particular has been shown to be highly effective in sub-milligram to low-milligram dispense quantities. Positive displacement modules are effective in the automated dispensing of a wider range of solids with varying physical properties, including sticky or oily solids. 27 Figure 1. 17. a) Axelsemrau Chronect outfitted with a Mettler-Toledo Auto Chem Quantos solid dispense module b) Chemspeed Technologies GDU-S SWILE with a positive-displacement module. Reprinted with permission from reference 81.81 Copyright 2020 American Chemical Society. Liquid handling modules aspirate and dispense solvents, liquid reagents, and stock mixtures. The simplest liquid handling systems consist of a pump for liquid displacement, valves for guiding the flow, a variety of tubes, and a dispensing head outfitted with a pipette or needle. For example, in a recent Hein lab project developing an automated platform for evaluating solubility, we used tubing to connect a syringe pump with the probe of a robotic arm. The arm was used to pick up needles on the probe to pierce through vial septa and transfer solutions.84 The two most common pump types observed in chemistry automation systems are peristaltic pumps and syringe pumps (Figure 1.18). A peristaltic pump displaces liquids by means of pressure waves generated through the compression of tubing by rotor-mounted rollers. Peristaltic pumps are relatively inexpensive and can be used for the continuous dispensing of larger volumes in the 28 milliliter range. Syringe pumps contain a syringe and plunger (typically driven by a stepper motor) outfitted with a valve for flexible flow path planning. These pumps are programmable for small, precise dispense volumes in microliter range. Valves are often used to enable different flow paths. Ports and positions are the main characteristics of valves (Figure 1.18). A port is the tubing connection point and a position is the directional state that the valve can achieve. Two-position rotary valves can have a large number of ports (typically six) where fluidic connections between adjacent ports are toggled by the position. Selector valves function in a similar manner, but in this case, a common port is connected to a variety of selectable ports through toggling the position. The tip of a liquid handling system plays an important role in the quality and accuracy of dispensing. Dispense heads can be outfitted with needles and pipettes. Needles have the advantage of being able to pierce through septa-capped vials, which are preferred for holding volatile organic solutions. Liquid handling modules are relatively simple to put together, but achieving reliability and robustness can be a challenge.85 Calibrating liquid handling modules for small volume dispenses of various liquid types, such as low-viscosity organic solvents, may be necessary. High-viscosity liquids may pose unique challenges that are insurmountable using common liquid handling modules. These liquids are best handled through positive displacement pipettes, which have been incorporated in the GDU-V tool offered through Chemspeed Technologies,86 and the Dragonfly Discovery robot offered through SPT Labtech.87 Clogging and air bubbles are other common challenges of liquid handling. When aspirating and dispensing from septa-capped vials, a vent is 29 required for pressure equalization. Some solutions to this problem include the utilization of special needles with an extra groove for pressure equalization and the utilization of pre-slit septa caps. Figure 1. 18. Liquid handling modules arranged in two configurations: A syringe pump, selector valve, and needle tip (top) and a peristaltic pump, 6-port, 2-position valve, and pipette tip (bottom). Figure by Paloma Prieto. 1.4.1.2 Stirring and temperature control The importance of stirring in chemical reactions is heavily influenced by the vessel configuration and the physical phases present in the reaction mixture. Homogeneous reactions consisting of a single liquid phase are less susceptible to mixing effects. Heterogeneous reactions consisting of 30 multiple phases, including gas-liquid, liquid-liquid or solid-liquid phases necessitate even and robust mixing.88 Automation for process optimization often involves the parallelization of reactions in order to maximize the experimental throughput. The Society for Biomedical Sciences (SBS) has standardized reaction microplates for the automated execution of multiple reactions in individual wells.89 However, the rectangular shape and footprint of the SBS format plate requires the careful consideration of the mixing module. Key considerations include mixing efficiency and uniformity. Figure 1.19 illustrates various stirring modules. The most ubiquitous stir plate designs for chemical applications utilize magnetic rotary stirring, but this technique is not uniform across rectangular SBS format plates, where stirring efficiency is reduced around the outer edges furthest from the magnet of the stir plate. Tumble stirring is instead more compatible with SBS format plates. V&P Scientific offers powerful tumble stirrers for this purpose, and these modules can be easily integrated into automation platforms.90 In addition to the stir bar based design, shakers such as those offered by Glas-Col eliminate stir bars from the process, preventing undesired effects from stir bars such as grinding of solids.91 Multi gram-scale automated lab reactors are typically with overhead stirring capabilities to better simulate commercial plant-scale equipment. These reactors offer advantages to microplate reactors with respect to hydrodynamics, mixing and surface to volume ratio, which tend to be important factors in the execution of heterogeneous reactions. Mettler-Toledo Auto Chem automated lab reactor systems provide both rotary and overhead stirring capabilities.92 31 Figure 1. 19. Magnetic and mechanical stirring modules impart different agitation homogeneity across a reaction plate, efficiency, and scalability. Figure by Paloma Prieto. Temperature control is key to the effective execution of synthetic reactions. Again, homogeneity of temperature is critical to experimentation, and this is generally achieved through circulator block/ chiller modules such as the circulator blocks offered by MeCour and Analytical Sales.93,94 High-efficiency chillers can be obtained from companies such as Huber.95 On the other hand, Peltier-based heating/ cooling capabilities can be utilized in automated reactors, such as those utilized in Integrity-10 systems.96 The advantage with the Peltier-based technology is that a larger number of reactor zones with individual temperature control can be accessed. A circulating chiller or fan with sufficient cooling power is still required to remove heat from the back of the Peltier elements. 32 1.4.1.3 Translocation The three separate classes of translocation technology discussed here can be packaged under the umbrella ‘robotics’ (Figure 1.20), the general purpose technology underpinning more and more complex laboratory automation solutions. Complex laboratory automation solutions typically consist of a collection of modules, thus, translocation between individual modules is an important consideration.77 The most accessible class of robotics is the Cartesian system, where an Z axis-actuated end effector (typically a needle probe) moves along rigid rails in the XY plane. Platforms geared toward high-throughput batch chemistry are often equipped with Cartesian robotics, offering the benefit of a turnkey solution but coming at a relatively high space penalty. For example, the Chemspeed Technologies SWING benchtop system is an enclosed box with a footprint of 1.5 m by 0.9 m, while the Chemspeed Technologies SWING XL measures 2.4 m by 0.9 m and comes on a custom mobile bench.97 In the latter case especially, it is evident that significant accommodations must be afforded which may require costly laboratory modifications such as removal of benches and cabinets. One outstanding advantage of this constrained box design is that it facilitates environment management, in that the entire enclosure can be a swept/ inerted atmosphere or simply connected to ventilation systems for removal of hazardous fumes. While Cartesian platforms excel at automated experimentation within their capabilities, the researcher must often adapt their experimental workflow to the actions the platform can execute. More articulated robotic systems, or robotic arms, can more closely mimic the actions of the researcher at the bench. Although the range of motions offered by robotic arms enables more 33 human-like interaction with the work environment, enabling access to a multitude of custom-designed modules or other commercial laboratory instrumentation, the tradeoff comes in that this is very much not a turnkey solution; significant effort goes into programming positions, timing, module actions, and the like to execute a workflow. SCARA (selective compliance assembly robot arm) platforms offer excellent locational precision and reproducibility within a limited operational range. Similar to a Cartesian system, the arm on a SCARA platform operates in any number of z-planes, but it differs in that the end effector is supported from a main pillar instead of overhead supports. This provides access to a greater range of motions, most notably the ability to move material laterally into environments inaccessible to an overhead system, such as loading a sample onto an enclosed analytical balance. Despite this advantage, movement locations are still somewhat limited, for example the work area is restricted to certain deck locations in the N9 robotic platform offered by North Robotics.98 Multi-axis robotic arms such as the Universal Robots UR3 allow for the widest range of motion.99 The layout of the workspace is limited only by the imagination of the researcher, providing access to human-like interactions with any number of analytical or processing modules. The movement of the robotics is not constrained to an enclosed box, rather only by the reach of the arm. Finally, the mobile robotic chemist of Burger and coworkers is the natural extension of a stationary articulated robotic arm to a mobile human analogue.30 Rather than using the arm to pass samples between modules/ stations, they took the approach of giving the arm wheels to transport samples around the laboratory for processing and analysis at conventional benchtop instruments. 34 Figure 1. 20. Robotic modules for translocation. Figure by Paloma Prieto. 1.4.1.4 Integrated Platforms The discussed modules can be combined in various configurations to develop a variety of integrated platforms, including micromole scale, high-throughput systems and millimole scale, medium-throughput systems up to mole scale, single-reactor systems. In the high-throughput space, vendors such as Unchained Labs,100 Chemspeed Technologies,97 and Tecan101 dominate. Unchained Labs and Chemspeed Technologies systems can be purchased with inertable enclosures, whereas Tecan systems are typically open to the atmosphere. These systems consist of a solid handler, liquid handler, stirring and temperature modules, a translocation module and a large robotic deck capable of holding multiple microplate reactors. Online analytical capabilities are also possible. These systems are best suited for parallel synthesis, reaction optimization and reaction profiling. In the medium-throughput space, vendors such as AmigoChem offer 10-reactor systems with individual stirring and temperature control modules, along with a liquid handling and translocation 35 module for automated sampling of reactions over time.102 These end user-friendly systems are best suited for reaction profiling. In the low-throughput space, vendors such as Mettler-Toledo Auto Chem92 and H.E.L. Group103 dominate with offerings of single or multiple automated reactors outfitted with various stirring options, temperature control, and reactor ports for reagent addition, automated sampling and online analytics. Mettler Toledo Autochem offers the EasySampler liquid handling module for automated sampling of reactions over time, as well as the ReactIR and ReactRaman probes for in situ reaction analysis. These systems are best suited for reaction optimization, reaction profiling, and process characterization experiments to define the parameters ranges to deliver consistent product quality. 1.4.2 Experimental Considerations Due to the specialized nature of synthetic reaction procedures, unique operational considerations dictate the process of automating experimental workflows. These include, but are not limited to: throughput and scale, chemical handling and compatibility, and sampling and analysis. Below we outline how these considerations impact the automation systems under development 1.4.2.1 Throughput and scale Increased throughput is a popular appeal of automation and no other area of chemistry automation has gained as much traction in recent years as high-throughput experimentation (HTE).77,88,104,105 HTE platforms push the limit of what is humanly possible to somewhere between 96 and 1536 reactions per day. When estimating throughput to evaluate the potential of a new automated system, a 4-digit reaction count per day is impressive, but may not necessarily be the best solution 36 to every synthetic challenge. This section focuses on identifying optimal throughput and scale based on the experimental goal, material availability, timing, and analysis. HTE in 96-well microreactors has been utilized extensively in reaction discovery and optimization.77,105 In reaction discovery and optimization studies, a diverse array of categorical parameters are examined to determine conditions that most efficiently affect the desired bond formation. HTE in 96-well batch microreactors is typically carried out in semi-automated fashion on a micromole scale, leveraging chemical libraries dispensed through automation, with experimental execution executed through manual pipetting.104 This allows for the maximum flexibility in execution while still automating the most cumbersome elements of the workflow. For example, in my 2016 report, the discovery and optimization of Suzuki-Miyaura coupling and asymmetric hydrogenation conditions for the enantioselective synthesis of α-methyl-β-cyclopropyldihydrocinnamates were executed through semi-automated HTE (Figure 1.21).106 Here, the automation of phosphine ligand dispensing significantly streamlined the workflow. In some instances, the full automation of 96-well HTE workflows can be preferable. For example, in my 2017 report, the optimizations of strong base mediated functionalization reactions were executed through fully automated HTE, largely owing to strong base mediated functionalization reactions requiring multi-step additions.107 As opposed to 96-well HTE, 384 or 1536-well HTE is almost always carried out in a fully automated fashion on a nanomole scale due to the unmanageable number of pipetting actions involved.108,109 Interestingly, as the throughput is increased, the scale is typically decreased in order to minimize material requirements, thus, increased throughput and miniaturization go hand in hand. 37 Figure 1. 21. a) HTE to discover optimal conditions for a Suzuki cross-coupling reaction b) HTE to discover optimal conditions for an asymmetric hydrogenation reaction. Reprinted with permission from reference 106.106 Copyright 2016 American Chemical Society. When considering throughput, timing is a key consideration. If a reaction is especially sensitive to timing, fewer reactions can be carried out in parallel because the system must be ready for use at specific times to execute specific steps (dispense, quench, sampling and analysis). This limitation can be addressed by using faster robots. The upper speed limit reported by Dreher and coworkers 38 on the nanomole scale was 6144 reagents in 30 min (Figure 1.22).108 While 30 minutes is not a limiting time period for multi-hour reactions, some reaction mixtures are highly active and can decompose rapidly. Dispense, sampling, and quench times can therefore pose significant challenges for batch setups. If the throughput does not allow for the timing requirements to be met, parallel experiments can be broken down into smaller parallel blocks for sequential execution. Figure 1. 22. HTE in 1536-well batch nanoreactors. From reference 108.108 Reprinted with permission from AAAS. 39 The sampling strategy is also an important factor in determining the optimal throughput. High-throughput experiments are typically sampled once at the end of each reaction. However, reaction profiling studies, where each reaction is sampled multiple times over pre-designated time intervals, can provide valuable mechanistic insights into the system under evaluation. Here, the sampling resolution is determined by the minimum sampling time, which is heavily influenced by the throughput and the sampling module. The final consideration is analysis of experimental outcomes. In HTE, the time spent analyzing the outcome far exceeds the time spent setting up reactions. In the above-mentioned study by Dreher and coworkers it took 30 minutes to dose all reagents, 1 hour for sampling and 52 hours to analyze all samples via UPLC. In order to accommodate this high analytical burden, a rapid MISER (multiple injections in a single run) LC-MS method and sample-pooling strategies were pursued to reduce the sample analysis time to 9 hours for 1536 experiments.108 Overall, the determination of appropriate throughput depends on the study goals, the material cost and availability, the scope of parameters under consideration, the timing of addition steps, the sampling strategy, and the appropriate analytical technique. Several statistical methods exist to narrow the search space if needed, and this can especially prove useful in high-throughput time course studies where short sampling interval times are required. 40 1.4.2.2 Chemical handling and compatibility Chemical handling and compatibility requirements encompass stock solution stability, order and rate of addition, reaction time, temperature control and reaction atmosphere control. In high-throughput batch automation, such requirements may be amplified due to longer than usual processing times. Failure to control these elements may lead to reproducibility issues or false negative or positive results. Certain automated workflows mirror the order of operations familiar to most bench chemists: dispensing of solids, followed by liquids, followed by specialty gas delivery, if applicable.110–112 However, parallelized micromole-scale workflows typically involve the automated dispensing of stock mixtures. I have implemented the latter method in the fully automated strong base screening workflow described earlier (Fig. 1.23).107 The key to successful implementation was to determine which stock mixtures were stable over the course of the experimental set-up. In this case, robust outcomes were achieved by preparing separate substrate, strong base, and electrophile stock solutions in compatible solvents with stringent humidity level control to prevent moisture-mediated base decomposition. Equally important was the order and timing of operations, where the substrate was first treated with base solution, aged for a set time period, and then treated with electrophile solution. This ensured full deprotonation prior to functionalization. Finally, equipment considerations such as temperature control through a circulating chiller and uniform stirring with a tumble stirrer were paramount to reaction control. 41 Figure 1. 23. Strong base screening workflow involving liquid stock mixture dispensing. Reproduced from reference 107 with permission from the Royal Society of Chemistry.107 Another demonstration of parallelized micromole-scale reactions involving the automated dispensing of stock mixtures was reported by Bristol Myers Squibb. In this study, humidity level control was only required during certain steps of the automated workflow. They observed that acetoin dimer 1.15 exhibited hygroscopicity in an automated annulation reaction optimization (Figure 1.24).113 A lack of humidity control during the weighing of acetoin dimer 1.15 led to a very high coefficient of variation (CV) of 60% in automated runs. The issue was corrected through weighing acetoin dimer 1.15 in a glovebox. This update to the workflow decreased the CV to 19%. 42 Figure 1. 24. Acetoin dimer 1.15 weighing was moved to a glovebox due to hygroscopicity. Reproduced from reference 113 with permission from the Royal Society of Chemistry.113 Finally, the compatibility of the dispensed chemicals with each robotic platform component requires careful thought. This is why it is best to invest in robots developed for chemistry applications. Even with chemistry-compatible robotic systems, we have encountered issues around stainless steel needle compatibility with highly acidic media. Surprisingly, most robotic platforms 43 in the chemistry space are outfitted with stainless steel needles, but several companies offer various inert coating options. 1.4.2.3 Sampling and analysis The most common analytical techniques to evaluate reaction outcomes are calorimetry, in situ infrared spectroscopy, in situ raman spectroscopy, NMR and liquid chromatography (LC) with either ultraviolet–visible spectroscopy (UV-Vis) or mass spectroscopy (MS) detection.114 In situ techniques are informative in that the analytical probes are capable of performing high frequency measurements over the course of the reaction.115,116 The drawback with in situ analytical probes is their large size, limiting their use to large-scale studies. Consequently, the preferred analysis method for micromole-scale reactions involves sampling for online or offline analysis. For effective analysis, the sampled reaction aliquot must be (1) representative of the reaction mixture as a whole and (2) remain stable and not react further once removed from the reaction mixture. Aliquot removal from a micromole-scale reaction typically involves the utilization of a sampling needle to puncture a sealed vial or reactor to draw one sample from each reaction at the assumed reaction end point. The drawback with this approach is the low frequency of measurements due to the solvent loss risks associated with frequent sampling. A much more desirable approach would be to increase the sampling frequency over the course of the reaction in order to generate more detailed reaction profiles.117 We recently worked with a vendor to identify a fluoropolymer cap mat that was demonstrated to prevent solvent loss upon frequent sampling and implemented this in the reaction profiling studies presented in subsequent chapters. 44 A key aspect to successful reaction monitoring is to effectively quench the reaction aliquots. A recent report from MSD indicates that a streamlined acidic quench protocol enabled DoE studies in combination with reaction profiling to fine-tune the conditions of the Vilsmeier Haack bromination of γ-Cyclodextrin to generate product 1.19 (Figure 1.25).112 The acidic quench was essential to generate accurate reaction profiles. Figure 1. 25. Development of an acidic quench for the optimization of the Vilsmeier Haack bromination of γ-Cyclodextrin. Adapted with permission from reference 112.112 Copyright 2021 American Chemical Society. 45 In instances where an effective quench cannot be developed, certain analytes are not stable, or real-time reaction monitoring is needed, online systems that enable near real-time analysis have become increasingly prevalent. The Hein lab has reported numerous systems that implement online analysis of reactions or crystallizations through various sampling methods and analytical techniques.118–124 Welch and coworkers have developed a similar online sampling capability for the Mettler-Toledo Auto Chem EasySampler, allowing for real-time MISER analysis of reactions.125 My 2019 report on the reaction profiling of the Suzuki-Miyaura coupling of E-alkenyl tosylate 1.20 with aryl boronic acid 1.11 under palladium catalysis highlights the utility of online HPLC analysis (Figure 1.26).3 In this report, online HPLC analysis allowed for the accurate quantitation of reactant and product concentrations without requiring sample stability over time. The comparison plots between online and offline HPLC analysis strikingly highlighted the disparity between the two techniques. The offline HPLC analysis revealed that the absence of an effective quench led to decomposition of the reactants in the presence of the palladium catalyst and oxygen upon sample aging. Thus, online analysis was essential to the accurate representation of the reactant concentrations over time. Ultimately, my reaction profiling studies unveiled palladium precatalyst activation of the rate-limiting step in the reaction, and a simple exchange with a different palladium precursor resulted in a five-fold increase in the reaction rate. 46 Figure 1. 26. Reaction monitoring with online HPLC analysis enables a more accurate reaction snapshot. Reproduced from reference 3 with permission from the Royal Society of Chemistry.3 The ability to integrate online analytics into batch chemistry automation opens up the avenue for feedback control. Feedback control brings an element of automated decision-making into the workflow through if-then logic at varying levels of complexity. Recently reported examples of feedback control include autonomous workflows based on GC analysis,30 machine vision,84 HPLC analysis and ReactIR analysis.126 Transitioning from automated to autonomous experimentation fundamentally transforms the interaction between chemists and instrumentation. In the new paradigm, the exact sequence of actions carried out by the automation is not predefined, but rather we define the experimental goal along with conditional instructions and enable the system to make 47 decisions autonomously. One potential application of feedback control is autonomous process optimization (APO), which will be discussed in more detail in the subsequent chapters. Before we move on to that discussion, we will provide some context on data considerations in chemistry automation. 1.4.3 Data Considerations High-throughput batch chemistry automation has the capacity to generate large datasets. In addition to this, chemistry automation tools are typically controlled by closed software architectures, and it is difficult to integrate disparate pieces of equipment.16,127 This section outlines ways in which equipment control and data flow can be handled in automation systems. 1.4.3.1 Equipment control and scheduling A significant component in any automation workflow is equipment control and scheduling. While the selected automation module may be physically capable of performing the desired tasks, this capability is irrelevant if the instrument is not controlled by accessible software or firmware. In most cases, each module has its own dedicated software, and it falls to the experimentalist to connect these disparate softwares. It is worth noting that in commercial integrated robots, all components are typically packaged under one software ecosystem, unless integration of a custom component not offered by the vendor is desired (Chemspeed Technologies, Unchained Labs, Tecan, Mettler-Toledo Auto Chem etc.). In some instances, the vendor software will accommodate the integration of custom components. 48 The capabilities of the software controlling the desired module will likely determine whether it can be leveraged in an automation workflow. A key factor is the presence or absence of an Application Programming Interface (API) for that software. If an API exists (and is well-documented), then integration is as straightforward as mapping the desired actions onto the automation software. It is possible that the hard work has been done already and an interface exists (either open-source, custom-built by users, or in rare cases supplied by the manufacturer). Software APIs vary greatly in complexity, ranging from simple one-way serial strings to bi-directional protocols with checksums for error catching across a wide variety of communication methods (TCP/IP, serial etc.). Generally, the more complicated the module, the more complicated the interface, unless the manufacturer has gone to great lengths to simplify their API. If no API exists for the module, then a hardware workaround will be required. If the module has a method of being remotely triggered, leveraging that method is straightforward. The module can be preconfigured to execute whatever actions are required of it, and an automation workflow can simply trigger that sequence of actions by, for example, a simple contact closure. In effect, the module’s entire workflow will be abstracted into a single signal from the perspective of the automation workflow. Note that if this is the selected integration method, careful consideration of the execution duration of the action will be required to ensure that the module’s portion is completed before the next requisite step. There is also the option to leverage an existing automation software package (e.g. LabView, Cycle Composer, Python), which can communicate with, control, and orchestrate many components. Although convenient, not every possible component or instrument is controllable by these 49 softwares, and it may become necessary to develop custom-built scripts in a programming language. If this becomes necessary, there is significant overhead involved in the development and implementation of these scripts. 1.4.3.2 Data flow In order to automate any aspect of data flow (acquisition, processing, and interpretation), careful consideration of the peripheral tools readily available for an automation platform is needed. The most important aspect for any data-flow automation is whether the automation software can read the data that is generated by the peripheral instrument. In many cases, data formats are vendor-specific and are not readily accessible by the automation software. Under these circumstances, the optimal method for automated data aggregation is through an external, custom-built script developed in a programming language. We have found Python to be a suitable language for effective data aggregation under various scenarios, with the added benefit of access to open-source data visualization and data science libraries. 50 Chapter 2: Autonomous optimization of a stereoselective Suzuki-Miyaura reaction 2.1 Introduction Recent advancements in computer science and automation technologies have led to the emergence of autonomous systems designed to generate and test hypotheses without the need for constant researcher intervention through automated feedback control.10–15 These systems typically involve three components: (1) an algorithm for experimental planning, (2) a robotic system for experimental execution, and (3) online analytics for experimental analysis. The system then executes a workflow with little to no human intervention. The level of human intervention varies based on the degree of hardware and software integration among the key components. In a fully autonomous system, a “closed-loop” is achieved through the integration of robotics, analytics and optimization algorithms, where the scientist can define the search space, and then press “go” (Figure 2.1). Closed-loop applications have ranged from biologically active compound discovery,64,128 to materials development,129–131 novel reaction scouting,20,132,133 and autonomous process optimization in flow reactors.10–20 Autonomous process optimization (APO) involves the human intervention-free exploration of a range of pre-defined process parameters to improve responses such as reaction yield, product selectivity and catalyst turnover number. The definition of a set of meaningful, broad, and unbiased process parameters is arguably the most critical aspect of a successful optimization. Work to date has focused on the multivariate optimization of continuous parameters such as temperature, 51 stoichiometry, and time; however, vital categorical parameters such as reagent, solvent or catalyst have rarely been incorporated. In fact, leading examples involving continuous and categorical parameter combinations have been limited to fewer than eight catalysts or ten solvents.21,22,26 Furthermore, in these examples, categorical parameter selection was guided through chemical intuition, potentially introducing an element of bias into the experimental design. Thus, in our view, categorical parameter selection in the context of autonomous process optimization remains an unsolved challenge. We envisioned developing a more systematic method for the selection of a broad and diverse set of categorical parameters to fully represent the chemical space, driving more effective optimization campaigns. We identified a stereoselective Suzuki-Miyaura cross-coupling reaction that would benefit from a tandem categorical and continuous parameter optimization (Scheme 2.1).106,134 Suzuki-Miyaura cross-couplings of alkenyl sulfonates or halides are presumed to proceed with retention of the alkenyl bond geometry,135 but in this example, we found that the alkene was subject to significant stereoinversion under ligand-free and electron-rich dialkylaryl phosphine-mediated palladium catalysis, selectively generating product 2.4. In contrast, we also observed that stereoinversion was partially suppressed under ferrocenyl bisphosphine-mediated palladium catalysis, facilitating a modest selectivity for product 2.3. Thus, stereoselectivity in this system appeared to be influenced by both the phosphine ligand selection and stoichiometry. Importantly, traditional phosphines preferred in Suzuki couplings, such as dialkylaryl phosphine ligands,136–139 appeared to facilitate the undesired stereoinversion pathway. Olefin bond geometry control was important in this example because the alkenyl bond configuration dictated the product stereochemistry under cationic ruthenium/ chiral phosphine-catalyzed asymmetric hydrogenation. The chiral cores that 52 resulted were designed as building blocks towards the syntheses of a variety of diabetes therapy candiates.106,134 Scheme 2. 1. Phosphine ligand influence on a stereoselective Suzuki-Miyaura coupling to generate the stereoinversion product (2.4) or stereoretention product (2.3). a Conditions: 10 µmol 2.1, 1 µmol 1,3,5-trimethoxybenzene, 20 µmol 2.2, 0.4 µmol Pd(ACN)2Cl2, 30 µmol K3PO4 (0.5M aq) in ACN (0.05M), 2 h at 25 °C. b,c Conditions: 10 µmol 2.1, 1 µmol 1,3,5-trimethoxybenzene, 11 µmol 2.2, 0.2 µmol Pd(ACN)2Cl2, 0.4 µmol L, 30 µmol K3PO4 (0.5M aq) in ACN (0.05M), 2 h at 25 °C. Ligand structures are provided in Figure 2.5. The goal of this project was to improve the yield of stereoretention product 2.3 through an autonomous process optimization campaign by exploring a categorical selection of phosphine ligands and continuous process parameters in tandem. Thus, we selected a Bayesian Optimization algorithm capable of proposing combinations of continuous and categorical process 53 parameters.69,72 We employed a Chemspeed SWING robotic system integrated with an Agilent high performance liquid chromatography (HPLC) instrument for the execution and online end-point analysis of parallel reactions 96-well batch microreactors (Figure 2.1). Recognizing the impact of phosphine selection on the optimization outcome, we employed a variety of categorical parameter selection strategies, including chemical intuition and 4D quantum mechanical descriptor clustering of 365 commercially available phosphines.55,58 Figure 2. 1. Autonomous process optimization platform for the human intervention-free optimization of categorical and continuous process parameters in 96-well batch microreactors with reaction end point analysis. 2.1.1 The Suzuki-Miyaura reaction The Suzuki-Miyaura reaction was developed by Akira Suzuki in 1979 and involves the palladium-catalyzed carbon-carbon cross-coupling between an aryl or alkenyl halide or sulfonate ester with an aryl or alkenyl boronate or alkyl trifluoroborate.140 Akira Suzuki, Richard Heck and Ei-ichi Negishi were awarded the 2010 Nobel Prize in chemistry for the discovery of metal-catalyzed 54 cross-coupling reactions. The Suzuki-Miyaura methodology has seen the highest utilization in modern synthetic organic chemistry due to its ease of experimental execution and the wide availability of bench-stable coupling partners (Figure 2.2).139 Figure 2. 2. The number of publications and patents on metal-catalyzed cross-coupling reactions (pre-1971 through 2010). Reproduced from reference 139 with permission from the Royal Society of Chemistry.139 2.1.2 Suzuki-Miyaura reactions of alkenyl sulfonate electrophiles A key strategy for the activation of ketones to generate electrophilic cross-coupling partners involves the generation of alkenyl sulfonates such as triflates, nonaflates, mesylates and tosylates 2.8 through alpha carbonyl deprotonation followed by enolate capture with sulfonic anhydride electrophiles (Scheme 2.2).141 The reaction conditions for the generation of alkenyl sulfonates can be tuned to impart stereoselectivity through the adjustment of base, solvent and temperature. Alkenyl sulfonates of this nature have been widely employed in a variety of cross-coupling 55 reactions, including Suzuki cross-couplings.106,142,143 Once subjected to the palladium-catalyzed reaction conditions, alkenyl sulfonates generally retain their olefin bond geometry. Scheme 2. 2. Alkenyl sulfonates as viable partners in Suzuki cross-couplings. 2.1.3 The general mechanism of Suzuki-Miyaura reactions with alkenyl electrophiles The proposed mechanism of a Suzuki coupling of an alkenyl sulfonate or halide with an aryl boronic acid or boronate ester begins with oxidative addition of a palladium(0) complex into the alkenyl electrophile 2.8 to generate the palladium(II) oxidative addition complex 2.10. Oxidative addition is followed by two potential pathways based on recent mechanistic studies. In pathway A, the borate species 2.11 generated from the boronic acid and base undergoes transmetalation with the oxidative addition complex 2.10. In pathway B, the oxo-palladium complex 2.13 generated from the oxidative addition complex and anionic base undergoes transmetalation with the boronic acid 2.14. Finally, the reductive elimination of the coupled product 2.9 from the palladium(II) complex 2.12 regenerates the active palladium(0) catalyst (Scheme 2.3).144–148 As illustrated in the catalytic cycle, the olefin bond geometry of the alkenyl sulfonate is presumed to be retained throughout each step. In other words, the Suzuki couplings of alkenyl sulfonates and halides are considered to proceed in a stereoretentive manner with respect to the olefin bond geometry. 56 Scheme 2. 3. Proposed mechanism of the cross-coupling of alkenyl sulfonate pseudo halides or halides with aryl boronic acids or boronate esters. 2.1.4 Stereoinversion in Suzuki-Miyaura reactions with alkenyl electrophiles The first example of alkene stereoinversion in a Suzuki reaction dates back to Akira Suzuki’s seminal report of the palladium-catalyzed couplings of alkenyl halides and alkenyl boronates in 1979.140 Here, Suzuki reported the Pd-catalyzed coupling of E-alkenyl boronates 2.15 with E-alkenyl halides 2.16, demonstrating retention of the olefin bond geometry in both coupling partners. Conversely, Suzuki also noted that the coupling of E-alkenyl boronates 2.15 with a Z-alkenyl halides 2.17 resulted in scrambling of the Z-alkenyl halide olefin bond geometry (Scheme 2.4). Suzuki proposed that the initially formed E,Z-diene 2.19 isomerized to the E,E-diene 2.18 under the Pd-catalyzed reaction conditions. Detailed mechanistic studies were not pursued. 57 Scheme 2. 4. First report of stereoinversion in a Suzuki coupling.140 Additional examples of alkene stereoinversion in palladium-catalyzed coupling reactions have been reported over the past decade. Three reports by Lipshutz and coworkers detail alkene bond geometry control through the fine-tuning of phosphine ligand in palladium-catalyzed Negishi,149 Stille,150 and Suzuki151 couplings of Z-alkenyl halides or sulfonates (Scheme 2.5). Similar alkene bond geometry control strategies were employed in in the stereoselective synthesis of tetra-substituted olefins from alkenyl sulfonate precursors by Genentech152 and MSD.143 Scheme 2. 5. Reports of phosphine ligand influence on stereoinversion under palladium catalysis in Negishi, Suzuki and Stille couplings.149–151 58 These reports hold in common that the stereoinversion pathways appear to proceed in the presence of palladium and phosphine ligands, and do not appear to proceed through Michael addition-elimination pathways. Rather, stereoinversion appears to proceed through a palladium complex that can be accessed from various intermediates in the Suzuki catalytic cycle (Scheme 2.6). Pathway A involves the generation of palladium -2 alkenyl complex 2.22. One could speculate that the electronic properties of the phosphine ligand could increase the single bond character of the double bond in the palladium -2 alkenyl complex, driving stereoinversion. Pathway B involves tautomerization to generate a zwitterionic palladium-carbene complex 2.23 in which the double bond is converted to a freely-rotating single bond. A highly stable zwitterionic complex could have long enough lifetime to drive significant stereoinversion. Pathway C involves the generation of a palladium hydride intermediate 2.24 through -hydride elimination from the Z-complex 2.20. The palladium hydride could then add across the double bond, generating a freely-rotating single bond in complex 2.25, prior to a second -hydride elimination-addition to regenerate the E-complex 2.21.153 Many of the described pathways are adapted from mechanistic studies around the carbopalladation of alkynes to generate isomerizable alkene complexes through carbon palladium syn insertion, generating intermediates similar in structure to Suzuki coupling intermediates from alkenyl halides and sulfonates.154–160 Aside from a few basic control reactions to exclude background isomerization in the absence of palladium and phosphine ligand, reports by Lipshutz, Genentech or MSD did not probe the propensity for one mechanistic pathway over the other. In some cases, 59 -hydrogens were not present in the alkenyl halide or sulfonate, eliminating the potential for pathway C to occur. Scheme 2. 6. Proposed mechanisms of stereoinversion in palladium-catalyzed couplings One of the most detailed mechanistic studies around the influence of palladium and phosphine ligand on alkene isomerization in the context of a Suzuki reaction came from Hultin and coworkers in 2018 (Scheme 2.7).153 Here, experimental studies ruled out the presence of a palladium hydride species to promote pathway C and computational studies favored pathway A over pathway B. 60 Scheme 2. 7. Proposed mechanism for stereoinversion in the Suzuki couplings of haloenones.153 2.2 Reaction profiling studies of a stereoselective Suzuki-Miyaura reaction In order to familiarize with the Suzuki reaction selected for autonomous process optimization, a number of automated reaction monitoring experiments were carried out on the Chemspeed robot (Figure 2.3). It was observed that under Pd(dppf)Cl2.CH2Cl2 catalysis, a 2:1 ratio of products 2.3 and 2.4 were generated and this ratio remained constant over time, eliminating the possibility of background product isomerization. Also, the Z-isomer of starting material 2.1 was not observed, diminishing the possibility of background starting material isomerization (although the possibility of its generation and rapid consumption could not be fully ruled out). More interestingly, spiking an equimolar amount of phosphine-free palladium(II) precursor (PdCl2 or PdOAc2) to Pd(dppf)Cl2.CH2Cl2 at the beginning of the reaction decreased the final ratio of products 2.3 to 2.4 to 1:1. Further, executing the reaction under phosphine-free palladium catalysis resulted in the exclusive generation of product 2.4, with no detection of product 2.3. This was a surprising finding, and led 61 to the hypothesis that the presence of phosphine-free palladium(II) and/or palladium(0) may play a role in the isomerization pathway. Thus, one could extrapolate that the phosphine to palladium ratio might play a critical role in determining stereoselectivity. The observation of exclusive stereoinversion product formation in the absence of phosphine ligand also ruled out phosphine addition/elimination to 2.1 or 2.3 as the isomerization culprit. Based on our literature review, only Hultin and coworkers have made similar observations around double bond isomerization under phosphine ligand-free palladium catalyzed cross-couplings, but there have been reports of double bond isomerization in the presence of Pd(II) salts in other contexts.161,162 As a final observation, a black precipitate formed in phosphine-free reactions upon full consumption of starting material 2.1, indicating Pd catalyst instability in the absence of the alkenyl sulfonate. Thus, stereoselective access of product 2.4 is still recommended in the presence of phosphine ligand. As mentioned in Scheme 2.1, electron-rich dialkylaryl phosphine-mediated palladium catalysis promotes the selective access of product 2.4. In light of the mechanistic complexity of this process, along with non-linear synergies associated with the presence of phosphine-free palladium, we turned to autonomous process optimization (APO) for empirical optimization to access product 2.3 in higher yield and selectivity. 62 Figure 2. 3. Reaction monitoring of the stereoselective Suzuki-Miyaura reaction under the catalysis of a variety of palladium complexes to better understand the alkene isomerization mechanism. Conditions: 100 µmol 2.1, 10 µmol 1,3,5-trimethoxybenzene, 110 µmol 2.2, 5 µmol Pd(dppf)Cl2.CH2Cl2, 5 µmol PdX2 (if applicable), 300 µmol K3PO4 (1.0M aq) in ACN (0.07M), 45 min at 25 °C. 2.3 Development of a high-throughput autonomous optimizer with end point analysis 2.3.1 Reactor selection and sampling strategy Successful optimization hinges on the identification of suitable automation equipment capable of effective experimental execution and analysis. Current research focuses heavily on custom-built continuous and segmented flow-reactor systems outfitted with online analytics for experimental 63 execution.10–20 While these state-of-the-art systems have enabled the rapid multivariate optimization of several processes, examples have still been limited to the sequential execution of fast, homogeneous reactions amenable to flow reactors. In this work, we expanded the autonomous process optimization toolkit to include a broader set of reaction methodologies by integrating off-the-shelf commercial robotic systems with online HPLC capabilities to carry out parallel batch chemistry in 96-well batch microreactors. In order to simplify the workflow, we analyzed these reactions at the assumed end point, which was estimated as two hours based on our prior reaction monitoring studies (Figure 2.3). We realized that certain process conditions could potentially result in incomplete reactions within the two hour timeframe, but decided that slow reactions were of lower priority than fast reactions, and that this sampling strategy would promote the determination of the optimal process conditions. 2.3.2 Optimization algorithm selection The key aspect of successful optimization is the selection of an effective optimization algorithm. Recently, Bayesian optimization (BO) algorithms have gained traction in the in autonomous chemistry realm, leading to a number of successful optimizations.30,31,70,163 BO is a sequential model based optimization approach that constructs a model of the objective function based on existing measurements to propose parameter points with promising predicted performance. The statistical approximation is refined with each measurement, resulting in an increasingly accurate model. The algorithm is configured to balance the exploitation of areas of promising performance with the exploration of areas of uncertainty through an acquisition function to allow for the determination of a global optimum output.67–72 One limitation of BO is that parameter point selection is typically sequential. However, the Phoenics72 and Gryffin69 algorithms developed by 64 Aspuru-Guzik and coworkers supplement fundamental concepts from BO with Bayesian neural network models in lieu of Gaussian process models to suggest parameter points for parallel experiments. In addition, Gryffin was developed specifically for the parallel optimization of categorical parameters through kernel density estimation. Given our interest in the autonomous optimization of categorical and continuous parameters in tandem through the execution of parallel batch reactions, the Phoenics and Gryffin optimization strategies were deemed ideal fits in our optimization workflow. Therefore, we initiated a collaboration with the Aspuru-Guzik group at the University of Toronto in order to integrate Phoenics and Gryffin into our optimization workflow. Our collaborators configured and ran the optimization algorithm from Toronto, and we executed the experiments remotely from the MSD labs in Rahway, New Jersey. 2.3.3 Integration of the closed-loop system The establishment of a closed-loop system required the integration of three main components: (1) ChemOS,164,165 the experimental scheduler for coordination of experiments proposed by the optimization algorithms (Phoenics and Gryffin), (2) Chemspeed SWING, the robotic system for automated experimental set-up, and (3) Agilent 1100, the HPLC-UV system for measurement of the experimental outcomes (Figure 2.4). The only hardware customization required was the integration of the Agilent HPLC-UV system with the Chemspeed SWING robotic platform. This integration was accomplished through the installation of an HPLC valve on the Chemspeed robot deck and incorporation of relay switches for triggering chromatographic resolution and photodiode array detection.3 The next step was to establish automated data flow from the experimental scheduler to the robot, and from the online analytical system back to the experimental scheduler for the ML algorithm to interpret results and propose subsequent experiments. In lieu of developing 65 a complex Application Programming Interface (API) among the three software components (ChemOS, Chemspeed Autosuite, and Agilent Chemstation), our group member Lars Yunker opted to develop a lightweight Python framework for data transfer between these components.166 The script translated ChemOS parameter suggestions into stock mixture dispense volumes, calculated product assay yields from HPLC peak area ratios to an internal standard and reported experimental measurements back to ChemOS. Figure 2. 4. Closed-loop system for autonomous optimization. Figure by Tara Zepel. The three main components to enable this closed-loop include (1) ChemOS to coordinate experiments and data-driven approaches, (2) Chemspeed SWING for automated experimental setup, and (3) Agilent 1100 to characterize the experimental outcomes. 2.3.4 Definition of the search space A set of categorical and continuous parameters was selected for potential impact on the reaction outcome, including phosphine ligand, phosphine to palladium ratio, palladium loading, 66 arylboronic acid equivalents, and reaction temperature (Table 1). Upper and lower bound selection for each continuous parameter was guided by chemical intuition52 but also designed to be sufficiently broad to explore a diverse set of responses. Ligand set selections varied from 12 up to 23 ligands, depending on the selection strategy. The optimization algorithms (Phoenics and Gryffin) were configured to maximize the yield of the E-product 2.3, minimize the yield of the Z-product 2.4, minimize the palladium loading, and minimize the arylboronic acid 2.2 equivalents, in that order (Table 2.1). Each objective was configured with a 10 % relative threshold that would only consider the next objective once that threshold had been achieved. This multi-objective optimization, or Pareto optimization design, was made possible through the implementation of the scalarizing function Chimera, also developed by Aspuru-Guzik and coworkers.76 Table 2. 1. Input and output parameters for optimization. Input parameters Type Range Unit Phosphine ligand (P ligand) Categorical 12 – 23 ligand Phosphine to palladium ratio (P/Pd) Continuous 0.5 – 4.0 ratio Palladium loading (Pd mol%) Continuous 1.0 – 5.0 mol % Arylboronic acid 2.2 equiv (ArBA equiv)* Continuous 1.0 – 2.0 equivalents Reaction temperature (Rxn temp) Continuous 10 – 40 °C Output parameters Objective Priority Unit E-product 2.3 assay yield (E-PR AY) Maximize First mol % Z-product 2.4 assay yield (Z-PR AY) Minimize Second mol % Palladium loading (Pd mol%) Minimize Third mol % Arylboronic acid 2.2 equiv (ArBA equiv)* Minimize Fourth equivalents *Note: The arylboronic acid 2.2 equivalents parameter and response was removed from the experimental design upon expanding the search space from 12 to 23 ligands. 67 2.3.5 Planning the experimental workflow Firstly, the number of reactions for the effective evaluation of all selected ligands under a reasonable number of continuous parameter points was estimated to reside somewhere between 120 and 192. Secondly, conversion to product was estimated to complete within two hours under the mean points of the defined continuous parameter ranges. Thus, if the maximum number of 192 consecutive reactions were carried out sequentially, the optimization campaign would take 16 days to complete. This lengthy duration would serve to obviate the benefits of autonomous optimization, and correspondingly, necessitated parallelization of the reactions. Therefore, the reactions were parallelized into loops of eight, allowing for a 192-reaction campaign to be completed within 24 loops over a more reasonable time period of four days. Conditions for the first loop of eight reactions were selected randomly. Subsequent conditions were determined autonomously by Phoenics and Gryffin as analytical results were returned, following a data-driven strategy. ChemOS, the experimental scheduler, parallelized the suggested reactions for the Chemspeed SWING system to dispense and initiate in 15-minute intervals. Analytical samples were aliquoted and acquired at the endpoint of each reaction, also in 15-minute intervals. Trial runs unveiled two instrument constraints that necessitated further enhancements of the optimization strategy. The first constraint involved the need to fix the reaction temperature across each loop of eight reactions given that these reactions were designed to be carried out within the same reactor block and timeframe. In order to accommodate this constraint, the capabilities of the Phoenics and Gryffin optimization strategies were extended to facilitate optimization with process constraints following the idea of a previously introduced basic process-constrained Bayesian optimization algorithm (pc-BO(basic)).167 This extension allowed the suggestion of a total of eight 68 different experiments where the temperature was fixed across one loop. The second instrument constraint was the inability of the Chemspeed robot to execute sub-microliter dispenses accurately; thus, the Python script was augmented to round calculated dispense volumes to the microliter level and update the suggested parameters with the executed parameters prior to returning analytical results to ChemOS. These enhancements allowed for the successful application of an algorithm in a constrained experimental setting. 2.3.6 Reproducibility experiments In order to evaluate the reproducibility and system performance, two sets of eight standard experiments were carried out within each reaction block of a 192-reaction optimization campaign. We determined that the standard deviation (SD) of measured 2.3 and 2.4 yields fell between 1-2 mol% and relative standard deviation (RSD) ranged between 6-8% (Scheme 2.8). This level of precision was determined to be acceptable for meaningful data interpretation. Scheme 2. 8. Standard experimental conditions for reproducibility testing. Conditions: 10 µmol 2.1, 1 µmol 1,3,5-trimethoxybenzene, 15 µmol 2.2, 0.3 µmol Pd(ACN)2Cl2, 0.4 µmol L2, 30 µmol K3PO4 (0.5M aq) in ACN (0.05M), 2 h at 20 °C. Average yields for 1st set: 2.3: 30(2)%; 2.4: 19(1)% and 2nd set: 2.3: 28(2)%; 2.4: 17(1)%. 69 2.4 Autonomous optimization results 2.4.1 Autonomous optimization with ligands selected through chemical intuition Initial ligand selection was carried out using chemical intuition around phosphines with the potential to accelerate palladium-catalyzed cross-couplings. Chemical intuition refers to insight arising from a combination of literature precedent and hands-on experience.136–139 Twelve ligands were selected, including trialkyl, triaryl, ferrocenyl and dialkylaryl phosphines. These ligands were evaluated in combination with four continuous parameters, including phosphine to palladium ratio, palladium loading, arylboronic acid 2.2 equivalents, and reaction temperature. The optimization campaign was carried out in fifteen loops of eight reactions, totaling 120 autonomous iterations carried out over 60 hours. Initial visualization of the two product yields highlights the strong propensity for stereoinversion under the majority of evaluated conditions, generating significant levels of product 2.4 with nine out of 12 ligands (L3-L6 and L8-L12, Figure 2.5). Despite this demonstrated susceptibility for stereoinversion, the optimization ultimately resulted in conditions to access product 2.3 in 65% yield and 1.6:1 E/Z selectivity upon 118 iterations, under Pd-L7 catalysis. Notably, the optimizer dedicated a higher number of iterations to L7 (DPPF), the top-performing ligand. Also, although initial results with L7 were not promising in iterations 11 (1% 2.3), 12 (26% 2.3) and 33 (6% 2.3), likely due to suboptimal phosphine to palladium ratios, the optimizer continued to sample this ligand exploratively, revealing a combination of continuous parameter points that resulted in the optimum conditions in iteration 118 (65% 2.3). Bearing in mind that the optimizer was not configured with any background knowledge around this reaction system except for the pre-defined 70 process parameter ranges, arrival at this optimum is an impressive demonstration of the power of algorithmic optimization. It is also worth noting the algorithm’s repeated sampling of L11 and L12 despite poor performance under a number of parameter point selections. This behavior could potentially be attributed to explorative sampling or optimism bias in the algorithm’s predictions. Finally, although the optimum conditions aligned nicely with those identified previously in manual searches, improvement to the yield of 2.3 was nominal (compared to 60% yield and 1.7:1 E/Z selectivity as shown in Scheme 2.1). This nominal improvement was attributed to ligand bias resulting from chemical intuition-based phosphine selection, thus, we embarked upon the exploration of a methodically selected set of ligands to access 2.3 in even higher yield. 71 Figure 2. 5. Parameters and results of optimization with ligands selected through chemical intuition in campaign 1. Conditions: 10 µmol 2.1, 1 µmol 1,3,5-trimethoxybenzene, 10 - 20 µmol 2.2, 0.1 - 0.5 µmol Pd(ACN)2Cl2, 0.05 - 2 µmol L, 30 µmol K3PO4 (0.5M aq) in ACN (0.05M), 2 h at 10 - 40 °C. 72 2.4.2 Autonomous optimization with ligands selected through molecular features To this end, we sought a systematic method for the selection of a diverse set of phosphines for autonomous evaluation. A particularly attractive approach would leverage computed 4D molecular features of phosphines as these have been applied to reaction optimization through predictive modeling.54,142,168–171 We initiated a collaboration with Matthew Sigman and coworkers at the University of Utah, owing to their expertise in leveraging computed 4D molecular phosphine features for ML-driven catalyst optimization, in order to systematically select a diverse set of phosphines for further evaluation. The Sigman team utilized 365 commercially available monodentate phosphines to define the chemical space (limiting the focus to monodentate phosphines in order to more effectively control the ligation state of palladium). For each phosphine, quantum mechanical features were obtained by computing molecular properties for a representative set of conformers using DFT (this amounts to 4D molecular representation). Then, K-means clustering was carried out on the first four principal components of this feature set to divide the chemical space into 24 regions (Figure 2.6).55,58 A single compound was selected from each cluster for experimental evaluation based on additional considerations such as availability, price, and anticipated stability (Figure 2.7). One cluster contained ligands that were deemed too challenging to implement due to unsuitable physical properties; therefore, candidates from this cluster were excluded from the experimental design. 73 Figure 2. 6. K-means clustering on the first four principal components of the molecular descriptor set for 365 commercial monodentate phosphines. Figure by Tobias Gensch. The chemical space is represented by a two-dimensional plot of the first two principal components. Each cluster is represented by color and highlighted boxes indicate selected ligands. Selected ligand structures are provided in Figure 2.7. Investigation of a larger ligand set necessitated the expansion of the optimization campaign to 24 loops of eight reactions, totaling 192 autonomous iterations executed over 96 hours (16 of which were designated as test reactions to assess reproducibility as shown in Scheme 2.8). In addition, the expanded ligand set also necessitated the removal of the arylboronic acid 2.2 equivalents parameter and response from the experimental design. Initial visualization of the two product yields highlights the strong propensity for stereoinversion under the majority of evaluated conditions, generating significant levels of product 2.4 with 14 out of 23 ligands (L4, L12-L19, L23, L27-L29 and L31, Figure 2.7). Despite this, the second campaign resulted in the 74 identification of optimal conditions to access product 2.3 in 73% yield and 2.5:1 E/Z selectivity upon 161 iterations, using L30 as the ligand. Given that the ML algorithm had no previous information to bias the search, and that ligand selection was unbiased, an improved yield of 73% highlights the potential of our empirical optimization approach. As in the first campaign, the algorithm dedicated a significant portion of iterations to the top-performing ligand, previously L7 (DPPF), now L30 (PhSPhos). Additional high-performing ligands also fell under the triaryl phosphine category, with both electron-rich (L20, L21) and electron-poor (L26) triaryl phosphines proving effective. Surprisingly, ligands with significant structural similarity to L30 (PhSPhos), including L28 and L31 (SPhos), did not selectively yield 2.3. 75 Figure 2. 7. Parameters and results of optimization with ligands selected through descriptor clustering in campaign 2. Conditions: 10 µmol 2.1, 1 µmol 1,3,5-trimethoxybenzene, 15 µmol 2.2, 0.1 - 0.5 µmol Pd(ACN)2Cl2, 0.05 - 2 µmol L, 30 µmol K3PO4 (0.5M aq) in ACN (0.05M), 2 h at 10 - 40 °C. 76 A deeper look into the influence of the continuous parameters on the yield of product 2.3 (Figure 2.8) revealed that phosphine to palladium ratios within the center of the studied range provided optimal outcomes. As predicted, lower phosphine to palladium ratios resulted in an increase in 2.4 yields, potentially due to the presence of phosphine-free palladium; while higher phosphine to palladium ratios resulted in an overall lack of reactivity, likely due to blocking of coordination sites necessary for effective catalysis. Conversely, performing the reaction at temperatures near the upper bound of the evaluated range proved most effective in driving 2.3 formation. Finally, although higher palladium loadings resulted in improved product 2.3 yields, the algorithm did not default to maximize the loading to drive up the yield. It appears that configuring the algorithm to minimize the palladium loading resulted in a constrained evaluation of this response and we were pleased to observe that the algorithm arrived at a more reasonable optimum of 3.8 mol% loading. Figure 2. 8. Three-dimensional plot of three continuous parameter selections color-coded for 2.3 yield in campaign 2. Figure by Tara Zepel. PhSPhos results are outlined. 77 Finally, comparison of the optimization results from the two campaigns superimposed on the monodentate phosphine ligand space clearly demonstrates the advantage of systematic ligand selection over chemical intuition-based ligand selection (Figure 2.19). A wider range of product 2.3 yields were observed through the systematic exploration of a diverse set of ligands, ultimately leading to the discovery of L30 as a superior ligand. This behavior can be attributed to the challenging nature of the stereoselective coupling under evaluation, where ligands typically employed in Suzuki-Miyaura couplings resulted in high conversion, yet poor E/Z selectivity. These results highlight the potential shadow that expert bias could cast on the development of creative solutions to atypical synthetic challenges, and the utility of unbiased study designs. Figure 2. 9. The maximum yield of 2.3 obtained for each monodentate ligand explored in campaigns 1 and 2 mapped onto the chemical space of 365 commercial phosphines. Figure by Tobias Gensch. The chemical space is represented by a two-dimensional plot of the first two principal components. Color indicates the maximum yield of 2.3 for each monodentate phosphine ligand. 78 At this stage, the only question that remained unanswered was whether the algorithm’s optimization performance could be improved if phosphines were encoded through their quantum mechanical features as opposed to one-hot encoding. In order to test this, a manually selected set of 15 4D features were incorporated in a third 192-iteration optimization campaign (See Figure 1.7 for the list of selected phosphine features). A similar optimum was reached, accessing product 2.3 in 74% yield and 2.6:1 E/Z selectivity upon iteration 159, again under Pd-L30 catalysis. Surprisingly, the algorithm did not appear to converge as clearly as in the previous campaign because fewer iterations focused on the top ligand and, in fact, a high number of iterations focused on unproductive ligands such as L14, L16 and L26 (Figure 2.10). This behavior may be attributed to unproductive bias introduced by the added features, solidifying that unbiased ligand selection was critical to optimization success. There is also the possibility that the optimization performance was impacted by the large number of added features. 2.5 Evaluation of E-selective ligands identified through predictive modeling With two 192-iteration data sets in hand, various modeling strategies were employed to predict additional phosphines that could also promote E-selectivity.58 Thus, ligands with the potential for E-selectivity were proposed based on a small ensemble of multivariable linear regression models, as well as proximity in chemical space. In line with the philosophy of combining exploitation and exploration, we chose six additional ligands that included structures both similar (e.g. L33, L34), and different (e.g. L36, L37) to any ligand in the training set, as well as predictions with low certainty (L38). Manual experiments employing these ligands were carried out utilizing the optimal conditions identified from autonomous optimization to measure the selectivity outcome (Figure 2.11). Additionally, L30 was employed in a control experiment. 79 Figure 2. 10. Parameters and results of optimization with ligands selected through descriptor clustering with 15 selected phosphine descriptors included in algorithmic optimization (campaign 3). Conditions: 10 µmol 2.1, 1 µmol 1,3,5-trimethoxybenzene, 15 µmol 2.2, 0.1 - 0.5 µmol Pd(ACN)2Cl2, 0.05 - 2 µmol L, 30 µmol K3PO4 (0.5M aq) in ACN (0.05M), 2 h at 10 - 40 °C. 80 Figure 2. 11. Manual experiments under optimized conditions with predicted E-selective ligands. Conditions: 10 µmol 2.1, 1 µmol 1,3,5-trimethoxybenzene, 15 µmol 2.2, 0.4 µmol Pd(ACN)2Cl2, 0.9 µmol L, 30 µmol K3PO4 (0.5M aq) in ACN (0.05M), 2 h at 35 °C. See Section A.8 for a detailed discussion of the predictive modeling methods. First, we were pleased to find that the experimental result of the manual experiment with L30 agreed very well with the results from the autonomous experimentation. Second, two ligands (L35, L36) out of six were identified that surpassed the 50% threshold for the yield of 2.3, providing a higher yield of 2.3 than any other ligand apart from L30 in the previous experiments under the 81 very first reaction conditions that were attempted. Further iterations of data-driven ligand suggestion, design of new ligands, and optimization of reaction conditions could possibly lead to even improved yields of 2.3 but that was not the focus of the current study. It is important to note that the newly identified E-selective ligands are structurally distinct from L30 and, thus, it would have been unlikely to arrive at these selections through chemical intuition. 2.6 Assessment of optimization performance It can be challenging to evaluate the performance of a multivariate Bayesian optimization because much of the algorithm’s decision-making behavior occurs in a black box. One way to gain insight into the algorithm’s performance is to measure the relationship between output parameters, such as 2.3 yield, and the algorithm’s tendency to explore or exploit. In the Phoenics and Gryffin algorithms, a “sample bias” term is utilized to measure the degree to which the algorithm is programmed to explore or exploit in each iteration. In the second campaign, an array of eight evenly-spaced sample bias values were selected from each iteration, ranging from -0.000104 (highly explorative) to 0.000104 (highly exploitative). Plotting the yield of 2.3 against experiment ID while color coding for sample bias revealed that a majority of the exploitative iterations focused on the top-performing ligand, PhSPhos (L30), leading to a significant yield improvement of 20% (Figure 2.12a). Plotting the average yield of 2.3 at each sample bias value showed that on average, negative explorative sample biases led to low 2.3 yields, while positive exploitative sample biases led to high 2.3 yields (Figure 2.12b). This balanced strategy allowed for determination of the global optimum over the local optimum because high-performing ligands could have been missed under certain continuous parameter selections such as high phosphine to palladium ratio. 82 Figure 2. 12. Two-dimensional plots of 2.3 yield for each experiment ID and average 2.3 yield for each sample bias value in campaign 2. Figure by Tara Zepel. a) Green indicates positive, exploitative sample bias values, while purple indicates negative, explorative sample bias values. PhSPhos results are outlined. b) Green indicates positive, exploitative sample bias values, while purple indicates negative, explorative sample bias values. 83 2.7 Qualitative modeling to determine process parameter influences Although the visual analyses presented up to this stage provided an understanding around the optimal phosphine ligand for 2.3 selectivity and the influence of phosphine to palladium ratio on 2.3 versus 2.4 selectivity, the multivariate nature of the data made it difficult to fully grasp continuous parameter effects across such a diverse phosphine ligand set. Therefore, we turned to random forest (RF) modeling, a machine learning technique that is especially effective in modeling categorical data, to tease out more trends.109 To assist with this effort, we enrolled the help of the MSD Biostatistics team to generate two RF models from the combined data from the second and third optimization campaigns. The computed 4D features that were incorporated in the third optimization campaign were also included in RF modeling in order to tease out potential phosphine ligand electronic and steric effects on the process outcome. The first model measured the influence of each parameter on 2.3 yield (Figure 2.14.a), and the second measured the influence of each parameter on 2.4 yield (Figure 2.14.b). The partial dependence plots (PDP) for 2.3 and 2.4 yield demonstrated that the phosphine to palladium ratio, by far, had the largest influence on selectivity for both products. Interestingly, the choice of phosphine ligand ranked quite low as a parameter influence in both models, but certain 4D phosphine features ranked high in each model.55 The second and third most influential parameters for 2.3 yield were temperature and the phosphine energy of solvation in CHCl3.172,173 The second and third most influential parameters for 2.4 yield were phosphine ligand pyramidalization (Figure A.1.1) and sterimol B5 (Figure A.1.2), notably, both of these are steric features. 84 Figure 2. 13. Random forest (RF) partial dependence plots (PDPs) of optimization parameters on a) 2.3 yield and b) 2.4 yield. IncMSE: Increase in mean squared error. 85 Analysis of the influence of phosphine to palladium ratio on both products showed that the predicted yield of product 2.3 was maximized with a phosphine to palladium ratio between 1.5 and 2.0 (Figure 2.14.a), and that the predicted yield of product 2.4 was maximized with a phosphine to palladium ratio close to 0.5, the lower bound of the phosphine to palladium ratio parameter range (Figure 2.15.a). This aligned with our reaction profiling observations demonstrating a phosphine-free palladium catalyzed stereoinversion pathway (Figure 2.3). Further, a deeper look into the influence of reaction temperature and computed phosphine solvation energy in CHCl3 showed a positive correlation between the yield of 2.3 and temperature, and a negative correlation between the yield of 2.3 and the phosphine solvation energy in CHCl3. The temperature trend potentially indicates a relative rate preference for PdLn-promoted stereoretention over phosphine free Pd-promoted stereoinversion. The observation that a lower solvation energy in CHCl3 led to higher 2.3 yields potentially indicates a higher partition coefficient of phosphine in the organic phase of the biphasic Suzuki system, increasing the level of PdLn species available for catalysis in the organic phase. Finally, a deeper look into the influence of the computed phosphine pyramidalization and Sterimol B5 parameters showed a negative correlation between the yield of 2.4 and phosphine pyramidalization and a positive correlation between the yield of 2.4 and phosphine sterics. Pyramidalization is inversely correlated with sterics in phosphines,174 thus, both correlations indicate that more sterically hindered phosphines drive the phosphine-free Pd-promoted stereoinverison pathway, most likely through ligand dissociation. 86 The combined reaction profiling data and RF analyses indicate that phosphine-free Pd is the main isomerization culprit. This finding supports that the isomerization may be occurring through Pathway A in Figure 2.6 through the generation of Pd -2 alkenyl complex. In the absence of phosphine ligand, backbonding from Pd to the alkene C-C π* orbital would be high, increasing the alkene single bond character. In the presence of phosphine ligand, backbonding from Pd to the P–C σ* orbital would compete, reducing the backbonding from Pd to the alkene C-C π* orbital, retaining more alkene double bond character. In addition, the extent of competing backbonding from Pd to the P–C σ* orbital would vary based on phosphine ligand structure, impacting a potential secondary PdLn-promoted stereoinversion pathway. Figure 2. 14. Random forest (RF) partial dependence plots (PDP) of a) Phosphine to palladium ratio on 2.3 yield; b) Temperature on 2.3 yield; c) Solvation energy on 2.3 yield. 87 Figure 2. 15. Random forest (RF) partial dependence plots (PDP) of a) Phosphine to palladium ratio on 2.4 yield; b) Pyramidalization on 2.4 yield; c) Sterimol B5 on 2.4 yield. yhat: predicted yield across all process conditions, including phosphine ligand choice. 2.8 Conclusion We have demonstrated the human-intervention free multivariate optimization of a stereoselective Suzuki-Miyaura coupling in batch through the autonomous evaluation of a large phosphine ligand set and continuous parameters in tandem. This success was accomplished through a series of technological advances. First, the implementation of a robotic system capable of carrying out parallel experiments with online analytics significantly reduced cycle times. Second, the development of seamless communication between the ML algorithm and robotic hardware established a closed-loop system. Third, the incorporation of Bayesian optimization algorithms, 88 here Phoenics and Gryffin, facilitated tandem categorical and continuous parameter optimization. Finally, the employment of computed 4D molecular features enabled the systematic and unbiased definition of the phosphine ligand search space. The application of these advances resulted in the rapid identification of optimal conditions to maximize the yield of product 2.3. It is relevant to ask what advantage autonomous optimization offers over more well-established process optimization strategies such as HTE (high-throughput experimentation) or DoE (design of experiments). Although we view algorithmic optimization as a complementary technology, we also believe that its advantage lies in the multivariate optimization of categorical and continuous parameters in tandem within the fewest number of iterations. To elaborate on this point, a theoretical comparison among the technologies in the context of this Suzuki-Miyaura optimization is warranted. The optimization results presented herein reveal that a distinct combination of phosphine ligand and continuous parameter selection is required for an optimal 2.3 yield, supporting the need for a tandem categorical and continuous parameter search. A typical experimenter would be unable to thoroughly investigate a large search space involving 23 categorical parameter choices in combination with three continuous parameters within a budget of 192 experiments without some sort of statistical experimental design tool such as DoE. However, under the parameter ranges defined for this optimization, DoE studies could only hope to reveal promising reaction conditions if at least a three-level three-factor design were chosen, in order to effectively tease out trends related to phosphine to palladium ratios, which were optimal under the center points. Such a design, however, would result in a total of 621 experiments (33 x 23 ligands) which is considerably higher than the 192 experimental budget to which we restricted our 89 campaigns. Thus, both brute-force HTE or DoE would be expected to require substantially more experiments than the strategy that we have implemented. We acknowledge that our comparisons to existing experiment planning strategies are merely theoretical, and that an experimental demonstration of the advantage of the Gyffin strategy would be preferable. However, previous baseline tests suggest the competitive advantage of our experimental planning strategy in the context of similar optimizations.69 In addition, although an equal balance of explorative and exploitative sampling behaviors were implemented in this work, the precise ratio of sampling behaviors could be fine-tuned to improve future optimization performance. Finally, we acknowledge that it may not be possible to rule out the possibility of optimism bias in the algorithm’s predictions in the absence of a comparison between predicted values and experimental results. However, the optimism bias of a prediction is not that significant in the context of a closed-loop system, where experimental results are automatically generated and the surrogate model updated with each loop. This ability to self-correct is one of the advantages of the closed-loop system. The autonomous optimization approach can easily be replicated to solve a multitude of multivariate process optimization problems. Once widely adopted, the technology has the potential to empower modern-day researchers to shift their focus away from routine experimental execution and towards higher-complexity problem-solving. Areas for future improvement include reaction the incorporation of reaction profiling analysis and enhancements to multivariate data analysis to drive deeper process insights, in essence, to learn what the algorithm revealed. 90 2.9 Methods 2.9.1 General Commercial reagents were purchased from Millipore Sigma, Oakwood, Combi-Blocks, Alfa Aesar, Acros, Strem, Apollo and Alichem; and used as received. Anhydrous solvents were purchased from Millipore Sigma and used as received. Aqueous solutions were deoxygenated through subsurface nitrogen sparge for a minimum of 30 minutes. 2.9.2 Equipment Autonomous optimization experiments were executed using a Chemspeed SWING robotic system equipped with a four-needle dispense head and four 1 mL syringe pumps to enable accurate dispenses at low volumes. Slurry dispensing was enabled through 0.8 mm needle inner diameters. Agitation was carried out through an integrated custom V&P scientific two-position tumble stirring module and temperature control was achieved through an integrated Huber Unistat chiller with temperature feedback control. Reactions were executed in Analytical Sales 96 well Paradox reactors. Online HPLC analysis was carried out through an integrated Agilent 1100 HPLC equipped with a photodiode array detector and a custom sampling valve installed on the robot deck. Compounds were purified on a CombiFlash purification system employing Redisep Rf Gold silica gel columns. 1H NMR spectra for characterization were acquired on a Bruker 500 MHz instrument (4 scans). Samples were prepared in DMSO-d6 and spectra were calibrated to the DMSO-d6 reference peak at 2.50 ppm. 13C NMR spectra for characterization were recorded on a Bruker 500 MHz instrument (512 scans). Samples were prepared in DMSO-d6 and spectra were calibrated to 91 the DMSO-d6 reference peak at 40 ppm. ESI+ MS for characterization were acquired on an Agilent 1290 UPLC-MS. 2.9.3 Procedures 2.9.3.1 Representative procedure for reaction profiling studies Stock mixtures were prepared in anhydrous ACN and manually dispensed to a 24-well metal block with 4 mL glass vial inserts under N2 atmosphere. To each 4 ml glass vial was dispensed E-tosylate 2.1 (100 µmol, 250 ul of 0.40 M stock solution), (3-(benzyloxy)phenyl)boronic acid 2.2 (110 µmol, 250 ul of 0.44 M stock mixture), 1,3,5-trimethoxybenzene (10 µmol, 100 ul of 0.10 M stock solution), Pd(dppf)Cl2 (5 µmol, 250 ul of 0.02 M stock solution), and/or PdX2 (5 µmol, 250 ul of 0.02 M stock solution), followed by anhydrous ACN (250-500 ul) to ensure a total organic solvent volume of 1100 µl. The 4 mL vial was capped and placed on the robot deck for automated sampling at the designated reaction temperature under 20 psig of N2 with 500 rpm agitation. A sample was aliquoted by the robot needle for online HPLC analysis. Then, an automated dispense of degassed aqueous K3PO4 (300 µmol, 300 µl, 1.0 M) was carried out to initiate the reaction. Additional samples were aliquoted by the robot needle for online HPLC analysis in 5 min intervals over a 45 min. 2.9.3.2 Representative procedure for test reactions Stock mixtures were prepared in anhydrous ACN under N2 atmosphere and placed on the robot deck for automated dispensing to a cap mat and metal cover-sealed 96-well metal block with 1 mL glass vial inserts at the designated reaction temperature under 20 psig of N2 with 500 rpm agitation. To each well was dispensed Pd(ACN)2Cl2 (0.25 µmol, 25 µl of 0.01 M stock solution), and L2 92 (PoTol3) (0.38 µmol, 19 µl of 0.02 M stock mixture), followed by 7 min of age time. Then, E-tosylate 2.1 (10 µmol) with 1,3,5-trimethoxybenzene (1 µmol) was dispensed (20 µl of 0.5 M/ 0.05 M stock solution), followed by (3-(benzyloxy)phenyl)boronic acid 2.2 (15 µmol, 30 µl of 0.5 M stock solution in degassed ACN 5% H2O), followed by anhydrous ACN (106 µl) to ensure a total organic solvent volume of 200 µl. Then, a dispense of degassed aqueous K3PO4 (30 µmol, 60 µl of 0.5 M stock solution) was carried out to initiate the reaction. This procedure was executed sequentially for each well within a loop of eight replicates in 15-minute intervals. Each replicate was time stamped individually, aged for 120 min, and sampled for online HPLC analysis. 2.9.3.3 Representative procedure for optimization reactions Stock mixtures were prepared in anhydrous ACN under N2 atmosphere and placed on the robot deck for automated dispensing a cap mat and metal cover-sealed 96-well metal block with 1 mL glass vial inserts at the designated reaction temperature under 20 psig of N2 with 500 rpm agitation. To each well was dispensed Pd(ACN)2Cl2 (0.1 - 0.5 µmol, 10 - 50 µl of 0.01 M stock solution), and phosphine ligand (0.05 - 2.0 µmol, 2.2 - 100 µl of 0.02 M stock mixture), followed by 7 min of age time. Then, E-tosylate 2.1 (10 µmol) with 1,3,5-trimethoxybenzene (1.0 µmol) was dispensed (20 µl of 0.5 M/ 0.05 M stock solution), followed by (3-(benzyloxy)phenyl)boronic acid 2.2 (15 µmol, 30 µl of 0.5 M stock solution in degassed ACN 5% H2O), followed by anhydrous ACN (0 - 138 µl) to ensure a total organic solvent volume of 200 µl. Then, a dispense of degassed aqueous K3PO4 (30 µmol, 60 µl of 0.5 M stock solution) was carried out to initiate the reaction. This procedure was executed sequentially for each well within a loop of eight experiments in 15-minute intervals. Each reaction was time stamped individually, aged for 120 min, and sampled for online HPLC analysis (Figure 2.15). 93 2.9.3.4 Representative procedure for online HPLC analysis Two polypropylene 96-well collection blocks sealed with a silicone mats were manually prefilled with 800 µl of ACN 10% aqueous pH 3.5 ammonium formate buffer and placed on the robot deck under 20 psig of N2. Upon reaching the sampling time, 10 ul of reaction mixture was aliquoted and dispensed into the 800 ul quench solution in the collection block. Upon needle-mixing, 40 ul of quenched sample from the collection block was aliquoted and injected to the on-deck sampling valve outfitted with a 5 µl loop. The valve was automatically switched to transfer the sample to the Agilent 1100 HPLC for analysis. Figure 2. 16. Image of the Chemspeed SWING robot deck during autonomous experimentation. 2.9.3.5 Representative procedure for manual reactions Stock mixtures were prepared in anhydrous ACN and pipet dispensed to a 96-well metal block with 1 mL glass vial inserts under N2 atmosphere. To each well was pipet dispensed Pd(ACN)2Cl2 (0.38 µmol, 38 µl of 0.01 M stock solution), and phosphine ligand (0.92 µmol, 46 µl of 0.02 M 94 stock mixture), followed by 7 min of age time. Then, E-tosylate 2.1 (10 µmol) with 1,3,5-trimethoxybenzene (1 µmol) was pipet dispensed (20 µl of 0.5 M/ 0.05 M stock solution), followed by (3-(benzyloxy)phenyl)boronic acid 2.2 (15 µmol, 30 µl of 0.5 M stock solution in degassed ACN 5% H2O), followed by anhydrous ACN (66 µl) to ensure a total organic solvent volume of 200 µl. Then, degassed aqueous K3PO4 (30 µmol, 60 µl of 0.5 M stock solution) was pipet dispensed. The block was sealed and agitated for 120 minutes at 35 °C under 500 rpm tumble stirring. Upon reaction completion, 10 ul of reaction mixture was aliquoted and quenched into 800 µl of acetonitrile 10% aqueous pH 3.5 ammonium formate buffer for offline HPLC analysis. 2.9.3.6 Procedures for the synthesis of noncommercial compounds Methyl 3-cyclopropyl-2-methyl-3-oxopropanoate 2.32: To a round-bottomed flask under nitrogen atmosphere was charged methyl 3-cyclopropyl-3-oxopropanoate 2.31 (40.0 g, 281 mmol), K2CO3 (58.3 g, 422 mmol) and CPME (100 ml). Methyl iodide (17.6 ml, 281 mmol) was added slowly and the reaction was agitated at 25° C for 1.5 h. Additional methyl iodide (17.6 ml, 281 mmol) was added slowly and the reaction was agitated at 25° C for an additional 16 h. K2CO3 was removed through filtration and the cake was washed with CPME (50 ml). The filtrate was concentrated to an oil that was purified by normal phase column chromatography to give methyl 3-cyclopropyl-2-methyl-3-oxopropanoate 2.32 in 59% yield.106 95 (E)-Methyl 3-cyclopropyl-2-methyl-3-(tosyloxy)acrylate 2.1: To an oven-dried round-bottomed flask under nitrogen atmosphere was added methyl 3-cyclopropyl-2-methyl-3-oxopropanoate 2.32 (26.0 g, 166 mmol) and THF (260 ml). The mixture was cooled to -78 °C and 1M NaHMDS solution in THF (200 ml, 200 mmol) was added over 20 min, maintaining the internal temperature below -60 °C, followed by a THF rinse (65 ml). The reaction was agitated at -78 °C for 20 min and Ts2O (65.2 g, 200 mmol) in THF (520 ml) was added over 20 min, maintaining the internal temperature below -60 °C. The reaction was agitated at -78 °C for 20 min, then warmed to 25 °C and agitated for 16 h. A thick slurry formed. The reaction was quenched with 750 ml of aqueous 0.5M NaHCO3 and the phases were separated. The aqueous layer was back-extracted twice with 250 ml of EtOAc. The organic layers were combined and washed with 500 ml of saturated aqueous NaCl, dried over MgSO4 and filtered. Upon concentration to an oil, the crude product was purified by normal phase column chromatography to give (E)-methyl 3-cyclopropyl-2-methyl-3-(tosyloxy)acrylate 2.1 in 31% yield. 1H NMR (500 MHz, DMSO-d6) δ 7.89 – 7.83 (m, 2H), 7.54 – 7.47 (m, 2H), 3.72 (s, 3H), 2.44 (s, 3H), 2.20 – 2.11 (m, 1H), 1.66 (d, J = 1.1 Hz, 3H), 0.72 – 0.61 (m, 2H), 0.58 – 0.47 (m, 2H). 13C NMR (126 MHz, DMSO-d6) δ 168.06, 156.54, 146.15, 133.84, 130.72, 128.04, 122.17, 52.54, 21.62, 15.14, 14.02, 6.96. MS ESI+: (m/z calc. for C15H18O5S, [M + H] = 311.1); found = 311.1. 96 (E)-Methyl 3-(3-(benzyloxy)phenyl)-3-cyclopropyl-2-methylacrylate 2.3 and (Z)-Methyl 3-(3-(benzyloxy)phenyl)-3-cyclopropyl-2-methylacrylate 2.4: To a reaction vial in a positive nitrogen pressure glove box was charged (E)-methyl 3-cyclopropyl-2-methyl-3-(tosyloxy)acrylate 2.1 (1.00 g, 3.22 mmol), (3-(benzyloxy)phenyl)boronic acid 2.2 (0.808 g, 3.54 mmol), and palladium precatalyst 2.33 (0.298 g, 0.322 mmol). Anhydrous ACN (15 ml) and degassed 1M aqueous K3PO4 (9.67 ml, 9.67 mmol) were added and the reaction was agitated at 25 °C for 8 h. To the reaction was added 10 ml of saturated aqueous NH4Cl and the organic layer was separated from the aqueous layer. The aqueous layer was back-extracted twice with 10 ml of EtOAc. The combined organic layers were washed with 10 ml of saturated aqueous NaCl, dried over MgSO4 and filtered. Upon concentration to an oil, the crude product was purified by normal phase column chromatography to give (E)-methyl 3-(3-(benzyloxy)phenyl)-3-cyclopropyl-2-methylacrylate 2.3 in 52% and (Z)-methyl 3-(3-(benzyloxy)phenyl)-3-cyclopropyl-2-methylacrylate 2.4 in 33% yield. 2.3: 1H NMR (500 MHz, DMSO-d6) δ 7.43 (d, J = 7.1 Hz, 2H), 7.38 (t, J = 7.4 Hz, 2H), 7.34 – 7.26 (m, 2H), 6.95 (dd, J = 8.3, 1.9 Hz, 1H), 6.57 – 6.54 (m, 1H), 6.52 (d, J = 7.5 Hz, 1H), 5.11 (s, 2H), 3.72 (s, 3H), 2.57 – 2.52 (m, 1H), 1.49 (s, 3H), 0.65 – 0.56 (m, 2H), 0.19 – 0.11 (m, 2H). 97 13C NMR (126 MHz, DMSO-d6) δ 169.85, 158.44, 150.78, 138.69, 137.44, 129.84, 128.87, 128.26, 128.16, 124.33, 121.09, 115.03, 114.36, 69.64, 51.90, 18.21, 14.70, 5.64. MS ESI+: (m/z calc. for C15H18O5S, [M + H] = 323.1); found = 323.1. 2.4: 1H NMR (500 MHz, DMSO-d6) δ 7.46 – 7.41 (m, 2H), 7.38 (td, J = 6.7, 6.2, 1.7 Hz, 2H), 7.35 – 7.28 (m, 1H), 7.22 – 7.12 (m, 1H), 6.89 (ddd, J = 8.3, 2.6, 0.9 Hz, 1H), 6.54 (dd, J = 2.4, 1.5 Hz, 1H), 6.49 (dt, J = 7.5, 1.0 Hz, 1H), 5.07 (s, 2H), 3.24 (s, 3H), 2.06 (s, 3H), 1.93 – 1.84 (m, 1H), 0.75 – 0.68 (m, 2H), 0.21 – 0.14 (m, 2H). 13C NMR (126 MHz, DMSO-d6) δ 169.66, 157.93, 148.12, 140.31, 137.59, 128.94, 128.85, 128.20, 128.05, 126.08, 121.40, 115.28, 113.78, 69.57, 51.33, 15.82, 14.50, 5.94. MS ESI+: (m/z calc. for C15H18O5S, [M + H] = 323.1); found = 323.1. (2,6-dimethylphenyl)diphenylphosphane L20: To a reaction vial in a positive nitrogen pressure glove box was charged Cu(OTf)2 (0.268 g, 0.742 mmol), diphenylphosphine oxide 2.34 (1.00 g, 4.95 mmol), anhydrous toluene (20 ml) and TMDS (1.76 ml, 9.89 mmol). The reaction was agitated at 100 °C for 5 h with needle venting to avoid pressure build-up. Additional TMDS (0.868 ml, 4.95 mmol) was charged and the reaction was 98 agitated at 100 °C for an additional 16 h with no venting. Upon cooling to 25 °C, to the reaction vial was added DMEDA (0.106 ml, 0.989 mmol), Cs2CO3 (3.22 g, 9.89 mmol) and 2-iodo-1,3-dimethylbenzene 2.35 (1.15 g, 4.95 mmol). The suspension was agitated at 110 °C for 48 h. The reaction was cooled to 0 °C and KOH (20.6 ml, 61.8 mmol) in MeOH (3N) was added slowly to maintain the temperature below 0 °C. The mixture was agitated at 25 °C for 5 h. The quenched mixture was removed from the glovebox and 30 ml water was added. The resulting solution was back-extracted three times with 50 ml EtOAc. The combined organic layers were washed with 50 ml of 1M aqueous HCl, followed by 50 ml of 0.5M aqueous NaHCO3. The organic layer was decanted to remove elemental copper, dried over Na2SO4 and filtered. Upon concentration to an oil, the crude product was purified by normal phase column chromatography to give (2,6-dimethylphenyl)diphenylphosphane L20 in 13% yield. 1H NMR and 31P NMR spectra in CDCl3 match spectra in reference 174.175 1H NMR (500 MHz, Chloroform-d) δ 7.40 – 7.25 (m, 11H), 7.11 (dd, J = 7.5, 2.7 Hz, 2H), 2.25 (s, 6H). 31P NMR (203 MHz, Chloroform-d) δ -15.46. 99 Chapter 3: Autonomous optimization of a visible light-promoted Wohl-Ziegler bromination reaction 3.1 Introduction Chapter 2 reports the autonomous optimization of categorical and continuous parameters in 96-well batch microreactors through the customization of a commercial robotic platform. Although this project led to the successful optimization of an industrially relevant transition metal-catalyzed cross coupling reaction, we were left with a number of open questions that we aimed to answer and development opportunities for a second autonomous optimization project. The first question revolved around the breadth of chemical processes that our system could optimize. Due to the inerted nature of the Chemspeed robotic platform, moisture and air sensitive processes were easily accommodated, but could we expand to processes that require additional process controls such as photon delivery in photochemical reactions? Recent reports of autonomous photochemical optimizations utilize flow chemistry reactors,176,177 however, high-throughput microscale batch reactors are more amenable to combinatorial optimizations involving a large number of categorical parameter combinations. The second question revolved around the optimization strategy. Our initial work relied on one algorithm, a novel Bayesian sequential model-based optimization algorithm that encodes categorical parameters through kernel density estimates and models the parameter space through Bayesian neural networks (Gryffin).69 We wondered whether it might be useful to implement a 100 number of complementary optimization strategies, as opposed to a single strategy, to solve more complex optimization challenges. This would allow for flexibility in algorithm implementation. The third question involved the output parameter reporting strategy. Our initial work relied upon reporting output parameters at a predetermined time point at which each reaction was assumed to reach completion. In reality, as process parameters are varied, it becomes highly likely to encounter cases in which this assumption is proven to be inaccurate. What if we could monitor each reaction over time to dynamically determine the true reaction end point, then report the process output parameters at that time point? Would this result in more meaningful comparisons between evaluations? In addition to this, how could this treasure trove of time course data inform the selection of optimal conditions, as well as mechanistic analysis? Finally, recognizing that the development of a closed-loop system requires a fair amount of development work, we sought to develop methods for more effective post-optimization analysis of multivariate optimization data. We were interested in extracting as much information as possible from the data generated in order to inform mechanistic questions. With these objectives in mind, we designed our second optimization platform capable of optimizing photochemical reactions through the integration of a custom Lumidox temperature-controlled reactor (TCR), outfitted with 48 LED-irradiated wells, the addition of an online UPLC with shorter run times for rapid time course analysis of each reaction, and the implementation of multiple open source sequential-model based optimization algorithms for the mixing and matching of complementary optimization strategies (Figure 3.1). 101 Figure 3. 1. Autonomous process optimization platform for the human intervention-free optimization of categorical and continuous process parameters in 48-well batch photoreactors with reaction time course analysis for dynamic output reporting and flexible optimization strategy implementation. Our collaborators at MSD Process Research & Development identified a photochemical reaction of interest that was in need of optimization: the visible-light induced radical benzylic halogenation of methyl pyridazinone 3.1 (Scheme 3.1). Although these reactions can be carried out thermally in the presence of radical initiators such as azobisisobutyronitrile (AIBN), the energetic nature of these initiators pose shipping concerns,178 thus, it is advantageous to utilize visible light as an initiator in large-scale radical bromination reactions. One of the key objectives was to reduce the gem-dibrominated side product in order to control the product quality. Additionally, the chemistry needed to perform in a robust manner, i.e. small changes in the parameter space would not lead to failures. Finally, radical decomposition pathways, also termed “cliffs,”179 needed to be avoided. With these requirements in hand, we began developing the autonomous platform and optimizing the process in tandem. 102 Scheme 3. 1. Radical benzylic halogenation reaction identified for autonomous optimization. 3.1.1 The Wohl-Ziegler bromination reaction The Wohl-Ziegler bromination of allylic and benzylic C-H bonds by radical bromination reagents was first reported by Wohl in 1919. Wohl described the bromination of an allylic substrate with N-bromoacetamide.180 The methodology was improved by Ziegler in 1942 through the implementation of N-bromosuccinimide (NBS) as a radical bromination reagent.181 Interestingly, NBS continues to be one of the most highly utilized radical bromination reagents due to its mild nature, ease of storage and handling, and chemoselectivity.182 Additional radical bromination reagents that are commonly employed include N-bromophthalimide183 and 1,3-dibromo-5,5-dimethylhydantoin (DBDMH).184 Allylic and benzylic chlorinations with N-chlorosuccinimide (NCS)185 and 1,3-dichloro-5,5-dimethylhydantoin (DCDMH)186 have also been reported. Wohl-Ziegler brominations are typically carried out in polychlorinated solvents such as carbon tetrachloride (CCl4) and dichloromethane (CH2Cl2) and require the presence of radical initiators such as azobisisobutyronitrile (AIBN), benzoyl peroxide, peroxybenzoic acid, benzil, or light (Scheme 3.2).182 Due to the toxic nature of polychlorinated solvents, attention has been given to the identification of alternative solvents that adhere to green chemistry principles such as 103 acetonitrile (ACN)179,184 ethyl acetate (EtOAc) and isopropyl acetate (iPrOAc).187 Care must be taken in the identification of alternative solvents because solvents such as amides, tetrahydrofuran (THF) and toluene contain functional groups that react with bromine or chlorine radicals.188 A recent multivariate analysis of solvents proposed by the American Chemical Society (ACS) green solvent selection guide identified dimethyl carbonate (DMC) as an ideal replacement for polychlorinated solvents in the Wohl-Ziegler bromination reaction.189 Scheme 3. 2. The general reaction scheme of a Wohl-Ziegler bromination reaction. The mechanism of the Wohl-Ziegler reaction has been debated since its initial discovery. Initially, the reaction was proposed to involve a succinimidyl radical,190,191 however, evidence suggests that small quantities of molecular Br2 in NBS undergo homolytic bond cleavage in the presence of radical initiator. Molecular Br2 is presumed to form through the hydrolysis of NBS with low-level moisture to generate HBr, which then reacts with another molecule of NBS to generate Br2 (Scheme 3.3).179,192,193 Bronsted acids have been employed to accelerate Wohl-Ziegler bromination reactions, likely through the promotion of molecular Br2 generation.184 Lewis acids such as ZrCl4 have also been demonstrated as viable accelerants in the Wohl-Ziegler bromination reaction.194 104 Scheme 3. 3. Mechanism of the Wohl-Ziegler photobromination reaction. 3.1.2 Wohl-Ziegler photobrominations in the synthesis of pharmaceutical compounds Recently, visible light-promoted Wohl-Ziegler bromination processes have been demonstrated in the large-scale synthesis of pharmaceutical intermediates. The preference for visible light-promoted initiation over chemical initiation is driven by the inherent instability of chemical initiators such as AIBN, which decompose to generate radical species, driven by the generation of N2, in the presence of heat or light. This propensity for decomposition can lead to variability in quality and accidental explosions during transportation and storage.178 Thus, photochemical bromination processes have the potential to be more reliable and safe alternatives to their thermal 105 counterparts. The main challenge with the execution of photochemical processes is efficient photon delivery. Continuous flow chemistry reactors with high surface-area-to-volume ratios and tubing diameters matched to the molar absorptivity of the reactants have been widely adopted to overcome this challenge.195 In 2018, researchers at GlaxoSmithKline recognized the potential for multigram-scale photochemical bromination processes and reported a detailed study around irradiation requirements for their efficient implementation in continuous flow reactors.196 In this report, tunable 405 nm pseudo-monochromatic LED lights were utilized to vary the light intensity for measuring its influence on the solution yield of brominated product 3.10. They determined that the light intensity (in mA) influenced the initiation period, where higher light intensities on the order of 200-500 mA reduced the initiation period, likely due to an increase in the homolytic bond cleavage rate of Br2. They determined that the incorporation of acetic acid (AcOH) as a co-solvent also reduced the initiation period, likely due to the acceleration of Br2 generation from NBS. Finally, they demonstrated a photochemical bromination on 30 g scale, resulting in the bromination of benzylic substrate 3.9 through irradiation with 405 nm LED lights at 350 mA light intensity for 45 minutes (Scheme 3.4). They noted that small-scale optimization experiments in batch were ideal for optimization, and that their results were translatable to large-scale synthesis in flow. Scheme 3. 4. Multi gram-scale demonstration of a benzylic photobromination in flow.196 106 In 2020, researchers at MSD demonstrated the first industrial, multi-kilogram scale implementation of a visible light-promoted Wohl-Ziegler bromination reaction.184 This feat was achieved through the implementation of a tubular plug flow reactor (PFR) to process 50 kg of benzylic substrate 3.11 through irradiation with 450 nm LED chips in 32 hours (Scheme 3.5). The researchers at MSD also provided evidence for Br2 serving as the bromine radical precursor through small-sale UV-Vis spectroscopy studies in batch, where irradiated reaction mixtures showed significant absorption at 450 nm, demonstrating clear spectral overlap with the absorption profile of Br2. The addition of catalytic citric acid further increased the irradiated reaction mixture absorption at 450 nm with a corresponding increase in the reaction rate (Figure 3.2).179 Scheme 3. 5. Multi kilogram-scale demonstration of a benzylic photobromination in flow.184 Figure 3. 2. Spectroscopy studies in the presence and absence of citric acid. Reprinted with permission from reference 184.184 Copyright 2022 American Chemical Society. 107 These recent reports exemplify that visible-light induced Wohl-Ziegler bromination reactions are highly efficient in allylic and benzylic halogenations towards the synthesis of pharmaceuticals. The reports also highlight the importance of optimizing physical parameters such as light intensity and wavelength, in addition to chemical parameters such as radical bromine source, additive, solvent, and temperature. With these potential parameters in mind, we began high-throughput experimentation studies to narrow down the categorical search space for autonomous process optimization of the transformation introduced in Scheme 3.1. 3.2 High-throughput reaction profiling studies to narrow the search space Because the categorical parameter space was so vast, with several reagent, additive and solvent options to choose from, five rounds of automated high-throughput reaction profiling studies were carried out with a Chemspeed SWING robot in order to narrow down the categorical search space for autonomous optimization. In total, 182 experiments were carried out, revealing several leads for autonomous optimization. In addition to lead identification, the high-throughput studies allowed the development of the ideal automated protocol and photoreactor configuration (a detailed account of the photoreactor development will be provided in section 3.4.1). Because the automated procedure was also under development, two reactor types were employed in the high-throughput studies. Rounds one and five were executed in a Lumidox II photoreactor irradiated at 405 nm wavelength, while rounds two through four were executed in a Lumidox temperature-controlled photoreactor (TCR) irradiated at 470 nm wavelength. The TCR offered superior temperature control (25-26 oC at room temperature) over the Lumidox II photoreactor (10-33 oC at room temperature). 108 Figure 3. 3. Five rounds of high-throughput reaction profiling to identify the ideal reagent, additive, additive loading, temperature and solvent for a radical benzylic bromination. The first round of screening explored halogenation reagents and additives. Bromination conditions were investigated through the screening of NBS and DBDMH along with 24 acid additives. The solvent (ACN), light wavelength (405 nm) and additive loading (10 mol%) were held constant. Optimal reactivity was observed utilizing NBS as the halogenation reagent (Figure 3.4). The reaction was very slow in the absence of acid additive and plateaued upon generating 50 LC area % product after six hours of reaction time. In the presence of several acid additives, significant rate acceleration was observed. For example, the addition of 10 mol% of anhydrous phosphoric acid (H3PO4) generated product 3.2 in 79 LC area % in 1.5 hours. The second best option appeared to be phenyl phosphonic acid (PPA), generating product 3.2 in 78 LC area % in 1.5 hours. This rate acceleration was accompanied by the generation of 5 LC area % of dibrominated side product 3.3. Poor reactivity was observed with DBDMH, even in the presence of acid additive (Figure B.1.1). 109 Figure 3. 4. Results with NBS in the first round of automated high-throughput reaction profiling for reagents and additives. LC area % of starting material 3.1 (blue), product 3.2 (green) and side product 3.3 (red) measured over 9 hours in 1.5 hour time intervals. Conditions: 63 µmol 3.1, 69.3 umol NBS and 6.3 µmol acid additive in ACN (0.25M) irradiated with 405 nm LEDs at 60 mW (level 1) intensity for 9 h at 10 - 30 oC. The second round of screening evaluated five alternative solvents to ACN, including AcOH, chlorobenzene (Cl-benzene), EtOAc, trifluorotoluene (CF3-toluene) and nitromethane (MeNO2). Polychlorinated solvents were excluded from this evaluation due to process safety concerns. These solvents were evaluated alongside four additives to ensure that a high-performing solvent-additive combination was not missed (Figure 3.5). Reaction performance in MeNO2 was comparable to performance in ACN in the presence of Bronsted acid additives. Further, MeNO2 outperformed 110 ACN in the presence of Lewis acid additives, but the solvent was deprioritized owing to safety concerns.197 Reaction performance in AcOH was promising, generating up to 71% of product 3.2 in 20 min reaction time, however, this was accompanied by 10-15 LC area % of gem-dibrominated side product 3.3 at reaction plateau. EtOAc proved to be a viable solvent, especially in the presence of Bronsted acids, but product 3.2 levels plateaued at a maximum of 67 LC area %, much lower than 81 LC area% in ACN. Finally, starting material 3.1 decomposed in Cl-benzene and CF3-toluene. Figure 3. 5. Results with NBS in the second round of automated high-throughput reaction profiling for solvents. LC area % of starting material 3.1 (blue), product 3.2 (green) and side product 3.3 (red) measured over 240 min in 20 min time intervals. Conditions: 63 µmol 3.1, 75.6 umol NBS and 6.3 µmol acid additive in solvent (0.25M) irradiated with 470 nm LEDs at 60 mW (level 1) intensity for 240 min at 25 oC. The third round of screening was carried out to understand the influence of additive loading. Four loadings were evaluated for five acid additives in two solvents, ACN and AcOH. The sampling interval was reduced to five minutes to observe the reaction progress in more detail. Reactions in 111 ACN were sluggish in the absence of additives (Figure 3.6). The addition of 5 mol% of phosphoric acid generated 80% product 3.2 at reaction plateau. Sulfuric acid proved to be an even more effective rate accelerant, generating 81% product 3.2 in 20 minutes at 2.5 mol% loading. Phenyl phosphonic acid (PPA) was close behind, requiring loadings of 10 mol % to drive the formation of 75 LC area % product 3.2 in 35 minutes. Finally, reactions with hydrochloric acid (HCl) and oxalic acid did not perform well. Reactions in AcOH all performed equally well, with no demonstrable additive impact (Figure B.1.2). It is likely that the excess AcOH simply drowned out any additive effects. Figure 3. 6. Results in ACN in the third round of automated high-throughput reaction profiling for additive loadings. LC area % of starting material 3.1 (blue), product 3.2 (green) and side product 3.3 (red) measured over 65 min in 5 min time intervals. Conditions: 63 µmol 3.1, 75.6 umol NBS and 0.79-6.3 µmol acid additive in solvent (0.25M) irradiated with 470 nm LEDs at 60 mW (level 1) intensity for 65 min at 25 oC. The fourth round of screening measured the impact of temperature on the reaction outcome and reduced the levels of gem-dihalogenated side product 3.3. As the reaction temperature was decreased, an induction period became visible with sulfuric acid, indicating that the rate of bromide 112 radical generation could be slowed down through active cooling. Reactions carried out in the presence of phosphoric acid appeared to plateau at lower conversions at lower temperatures. Reaction rates with PPA were quite slow at temperatures below 15 oC. Sulfuric acid appeared to be the least sensitive to variations in temperature, however, the levels of gem-dihalogenated impurity 3.3 remained in the 5 LC area % range upon generation of >80 LC area % of product 3.2. Figure 3. 7. Results of the fourth round of automated high-throughput reaction profiling for temperature. LC area % of starting material 3.1 (blue), product 3.2 (green) and side product 3.3 (red) measured over 110 min in 10 min time intervals. Conditions: 63 µmol 3.1, 75.6 umol NBS and 3.2-6.3 µmol acid additive in solvent (0.25M) irradiated with 470 nm LEDs at 60 mW (level 1) intensity for 110 min at -25, -15, -5, 5 or 15 oC. In order to ensure that that micromole-scale bath conditions translated well millimole-scale flow conditions, the preferred conditions for scale-up of photochemical reactions due to large surface area to volume ratio and ideal path length for photon flux,195 our collaborator in the MSD Flow Chemistry group scaled up the optimal conditions identified in high-throughput reaction profiling 113 studies (10% H2SO4 with NBS in ACN). Three temperatures were assessed between 10 and 30 oC, and the temperature influence was determined to be minimal on a large scale as well. 1.5 g of pyridazinone 3.1 was passed through a Vaportec 3 ml reactor with a flow rate of 0.5 ml/ min for 65 min at 10 oC to generate product 3.2 in 69% assay yield and product 3.3 in 13% assay yield, with 5% starting material 3.1 remaining, thus, the mass balance was determined to be 87% (Figure 3.8). This experiment confirmed that micromole-scale batch results could be translated to millimole-scale flow. Figure 3. 8. Scale-up experiments in a Vaportec photoreactor. As we narrowed down the reaction parameters, we began thinking about the post reaction work-up process for the purification of product 3.2 and realized that straightforward succinimide 114 removal through water washes could be implemented if the reaction were performed in a water-immiscible solvent in place of ACN. As discussed in the section 3.3.1, there are not too many solvent options other than polychlorinated solvents reported in the literature for the execution of Wohl-Ziegler reactions. Polychlorinated solvents pose tremendous health risks on a large-scale, thus, these were not even considered as viable options, despite their water-immiscibility. We therefore turned to the ACS Green Chemistry division’s online solvent section database, which featurizes a list of 272 solvents based on chemical, physical, safety, health and environmental (SHE) considerations.198,199 The guide applies principal component analysis (PCA) to flatten the resulting multidimensional data set, allowing for the determination of solvents similar to any high-performing solvent of interest. The database classifies solvents, based on an adjusted SHE rankings, as green (recommended), amber (problematic), red (hazardous) and black (highly hazardous). Once we filtered out “red” and “black” solvents that posed SHE risks, we narrowed our options down to a list of seven water-immiscible solvents with properties similar to those of ACN. Upon evaluating these in the presence of the top-performing additives, phosphoric and sulfuric acid, in anhydrous and 2% water-wet solvent, we found dimethyl carbonate (DMC) to be a viable water-immiscible solvent option, generating product 3.2 in 86% LC area % upon reaction plateau, along with the generation of 5 LC area% side product 3.3. It is also important to note that ACN is classified as problematic in the database, while DMC is classified as green (Figure 3.9). 115 Figure 3. 9. Results of the fifth round of automated high-throughput reaction profiling for water-immiscible solvents. LC area % of starting material 3.1 (blue), product 3.2 (green) and side product 3.3 (red) measured over 120 min in 20 min time intervals. Conditions: 63 µmol 3.1, 75.6 umol NBS and 6.3 µmol acid additive in anhydrous or 2 volume % water-wet solvent (0.25M) irradiated with 470 nm LEDs at 60 mW (level 1) intensity for 120 min at 10 - 30 oC. 3.3 LED NMR studies to understand reaction plateauing The automated high-throughput reaction profiling studies discussed in section 3.2 unveiled an important observation: each reaction reached a conversion plateau prior to full starting material consumption. For example, in the fifth round of reaction profiling (Figure 3.9), reactions plateaued at conversions anywhere from 14% (with NBS and phosphoric acid in anhydrous methyl isobutyl ketone (MIBK)) to 96% (with NBS and phosphoric acid in anhydrous DMC). This observation raised a number of important questions. Why did reactions reach conversion plateaus and how were the plateau levels influenced by the process conditions? We suspected that bromination reagent decomposition had something to do with the conversion plateaus, but did not have a full grasp of the decomposition mechanism. Lack of understanding around a decomposition mechanism posed a significant risk for large-scale synthesis, potentially preventing the 116 implementation of effective process control strategies. We therefore embarked upon a collaboration with the MSD NMR group, where an LED-illuminated NMR spectroscopy technique for monitoring photochemical reactions had been developed in 2019 and implemented in the monitoring of multiple photochemical reactions,200–202 including a recently reported Wohl-Ziegler bromination.179 The collaboration commenced with studies to better understand the reaction plateau through monitoring the reaction species over time. Although UPLC-MS analysis allowed us to monitor starting material 3.1, product 3.2 and side product 3.3, we were not able to effectively monitor NBS and succinimide levels due to lack of strong chromophores in these species. NMR proved to be a complementary method for gaining an understanding of the fate of NBS. LED NMR experiments were carried out with 10 mol% phosphoric acid in ACN, at 15 and 35 oC, through 445 nm blue light irradiation. Two experiments were carried out at 15 oC that monitored starting material 3.1, product 3.2, dibrominated side product 3.3, NBS and succinimide concentrations over time. The first experiment was a light-dark study, where the reaction mixture was irradiated for 10 minutes, aged in the dark for 10 minutes, and then irradiation resumed for 100 minutes (Figure 3.10.a). No changes in reaction species concentrations were observed over the 10 min dark period, indicating the absence of a dark reaction or decomposition pathway. Overall mass balance with respect to 3.1, 3.2, and 3.3 remained steady over time and the mass balance with respect to NBS and succinimide also remained steady over time. The second experiment was a reaction monitoring study under constant irradiation. Here, we noted that 20 mol% of the NBS had converted to 117 succinimide prior to initiation and that the reaction plateaued due to full consumption of the remaining NBS prior to starting material consumption (Figures 3.10.b and c). Figure 3. 10. Reaction monitoring at 15 oC under light-dark cycling and constant light. Conditions: 1 equiv 3.1, 1.2 equiv NBS and 10 mol% H3PO4 in ACN-d3 (0.07M) irradiated with 445 nm LEDs for 80-120 min at 15 oC. An additional experiment was carried out at 35 oC that monitored starting material 3.1, product 3.2, dibrominated side product 3.3, NBS and succinimide concentrations over time. At higher temperature, not only did we observe that 18 mol% of the NBS had converted succinimide prior 118 to initiation, but also observed a reduction in the mass balance related to NBS and succinimide over time, indicating a decomposition pathway at higher temperature (Figure 3.11). No obvious intermediates large enough to account for this decrease in mass balance that were identified in the NMR spectra. Figure 3. 11. Reaction monitoring at 35 oC under constant light. Conditions: 1 equiv 3.1, 1.2 equiv NBS and 10 mol% H3PO4 in ACN-d3 (0.07M) irradiated with 445 nm LEDs at for 30 min at 35 oC. In order to understand the early formation of succinimide further, we prepared a mixture of starting material 3.1 (70 mM) and NBS (85 mM) in anhydrous ACN and observed that 15 mM of succinimide had already formed prior to our first NMR measurement. This concentration remained constant over 20 hours in the dark (Figure 3.12.a). In order to rule out the possibility that the source of succinimide was the reagent bottle, we prepared a 70 mM solution of NBS from the same reagent bottle and observed only 0.6 mol% succinimide. Similarly, the addition of H3PO4 to NBS resulted in a measurement of 0.5 mol% succinimide in solution. Interestingly, the addition of 119 starting material 3.1 to NBS resulted in the immediate formation of 18.6 mol% succinimide and the addition of starting material 3.1 and H3PO4 to NBS also resulted in the formation of 18.4 mol% succinimide (Figure 3.12.b). Similar observations were made when DMC was utilized as the solvent (Figure 3.12.c). We speculate that pyridazinone 3.1 might form an N-brominated complex in equilibrium, and we are unsure whether this species would serve as an effective bromination reagent. Reaction plateau variability could be based on changes in the equilibrium distribution between NBS and the proposed N-bromopyridazinone species under varied process conditions. Figure 3. 12. NMR studies of NBS with the addition of various reaction species in solution. At this time we felt that we had gathered enough information around the parameters of interest for autonomous process optimization and had also gained an understanding of the optimal experimental procedure. The LED NMR studies allowed us to determine that the preparation of stock mixtures containing pyridazinone 3.1 and NBS could potentially lead to plateau variability 120 issues, and thus, all future stock solutions for automated experimentation were prepared individually for each reaction component. 3.4 Development of a high-throughput autonomous photochemistry optimizer This section details the development of high-throughput autonomous photochemistry optimizer with time course analysis through the modification of a Chemspeed SWING XL robot with a particular focus on the integration of a high-throughput photoreactor with automated light intensity and temperature control, the integration of a tumble stirring system for uniform agitation, and the integration of an online UPLC analysis system for rapid outcome analysis (Figure 3.13). Figure 3. 13. Components of a Chemspeed SWING XL system for high-throughput autonomous photochemistry optimization with time course analysis. Also discussed is the implementation of a plateau detection algorithm for dynamic reaction end point determination and the selection of open source optimization algorithms for autonomous 121 optimization. Finally, the data integration strategy among the various hardware and software components for rendering a fully autonomous system will be described. 3.4.1 Photoreactor development Photons are a reagent in photochemical transformations, thus, effective and robust photon delivery is a critical aspect of automated photochemical reaction execution. We provided some discussion around the implementation of flow reactors in large-scale photochemical syntheses, but have not dedicated much discussion to the implementation of batch reactors in photochemical process optimization. Popular systems for batch photochemistry include the Penn PhD system,203 which can accommodate one millimole-scale reaction, as well as the SynLED and TAK-120 systems, which can accommodate up to ten millimole-scale reactions.204 When it comes to miniaturized, high-throughput photoreactor systems, the Lumidox photoreactor systems from Analytical Sales has seen widespread implementation in the photochemistry field.205 Here, up to 96 micromole-scale reactions can be executed in parallel, with one LED dedicated to each reaction well. The Lumidox LED panels, coupled with glass-bottomed 96-well Paradox photoreactors, have been reported in a number of high-throughput photochemistry optimizations.206,207 Two generations of the Lumidox LED panels exist, with the Lumidox II panels offering higher light intensities than the less powerful Lumidox I panels. Due to the wide range of LED light wavelengths and intensities available with the Lumidox II panels, we selected these for evaluation and characterization. 122 The parameters we were interested in characterizing were efficient photon delivery with uniform temperature control. We were aware that Lumidox II LED panels could lead to well temperature increases up to 25 °C at the lowest light intensity, and therefore initiated our studies at level 1 light intensity (60 mW per well). We implemented a temperature feedback loop to maintain the photoreactor temperature at 25 °C through active cooling of the base of the LED array with a chiller-cooled circulator block. We monitored the photoreactor temperature with the LED panel on for 60 minutes, then off for 60 minutes, and repeated this cycle six times. Upon switching the LED array on, the photoreactor temperature increased to 33 °C, and upon switching the LED array off, the photoreactor temperature decreased to 10 °C (Figure 3.14). We attributed these temperature swings to ineffective cooling of the photoreactor, as the chiller-cooled circulator block was only in contact with the base of the LED panel. Figure 3. 14. Temperature monitoring of the Lumidox II LED panel with the Paradox photoreactor. We therefore turned to Analytical Sales for the development of a more efficiently cooled photoreactor system that could access all five light intensities (60-385 mW) while maintaining a photoreactor temperature of 25 °C. Analytical Sales moved forward with the design and prototyped 123 a custom photoreactor with cooling channels incorporated between the rows of wells. In order to accommodate these cooling channels, the number of wells were decreased from 96 to 48. The custom photoreactor that was aptly named the temperature-controlled reactor (TCR). We implemented a temperature feedback loop to maintain the TCR temperature at 25 °C. Here, in addition to active cooling of the base of the LED array with a chiller-cooled circulator block, a tubing connection was established to flow the chiller fluid through the TCR cooling channels. We monitored the photoreactor temperature with the LED panel on for 30 minutes, then off for 30 minutes, at each light intensity level from 60 mW per well through 385 mW per well. With the implementation of the TCR cooling channels, excellent temperature control was achieved across all five light intensity levels, and the temperature was maintained between 24 and 26 °C, measured at three well positions (Figure 3.15). With these results in hand, we integrated the Lumidox TCR LED controller, the Huber circulating chiller and a V&P scientific tumble stirrer through RS232 connections to enable the direct Chemspeed Autosuite software control of illumination, cooling and stirring. With this, we were ready to optimize photochemical reactions in high throughput. Figure 3. 15. Temperature monitoring of the Lumidox TCR photoreactor. 124 3.4.2 UPLC integration As exemplified in our automated high-throughput reaction profiling studies, reaction profiling data can provide deeper levels of mechanistic understanding than reaction end point data. Moreover, reactions involving the generation of a side product as a function of product concentration require sampling as close to the reaction end point as possible, where the side product would theoretically measure highest. Dynamic reaction end point determination through the implementation of UPLC analysis coupled with a plateau detection algorithm would allow for more accurate reaction end point detection and subsequently more effective assessment of process parameter impacts on the process outcome. In order to carry out reaction profiling for dynamic end point determination of 48 autonomous reactions, we installed an ultra-high-pressure sampling valve on the Chemspeed SWING XL platform and connected this valve to the column switching valve of an Agilent 1290 UPLC for rapid automated analysis. The development of a three minute UPLC method made it possible to aliquot and analyze up to 12 samples from each reaction in less than one hour. Thus, 48 reactions could be executed and profiled in 48 hours. 3.4.3 Plateau detection algorithm for dynamic reaction end point detection The next challenge was to develop an effective real time plateau detection method. For this, we collaborated with the MSD Biostatistics team, owing to their deep expertise in statistics and machine learning. The MSD Biostatistics team developed a number of approaches for real time plateau detection based on the following assumptions: (1) the experimental outcome Y(t) is measured sequentially at different timepoints t=(t0,t1,t2…), (2) the experimental outcome at each 125 timepoint is denoted as Y=(Y0,Y1,Y2…), (3) the experimental conditions are denoted as X=(X1,X2,X3,...), (4) the goal of real time plateau detection is that at any time t, given the experimental conditions X, and all previous measurements {Y(s)}s<t, to decide whether the experiment has reached a “plateau” and can be stopped. Given these assumptions, three real time plateau detection methods were evaluated against existing data. 3.4.3.1 Algorithm 1 (maximum negative difference) The maximum negative difference approach assumes that after reaching a plateau, there are still fluctuations across time, assuming the maximum negative difference of Y(t) is caused by random fluctuations. The experiment reaches a plateau at t if |Yt−Yt−1|≤ δ, where the threshold is computed from previous data by: δ=max∀ {Ys < Ys−1}{|Ys−Ys−1|}. 3.4.3.2 Algorithm 2 (plateau variability) The plateau variability approach assumes that after reaching plateau, assuming Yt∼N(μ,σ2), then δY=Yt−Yt−1∼N(0,2∗σ2). The 95% confidence interval of δY is: ±1.96∗√2∗σ. The experiment reaches plateau at t-1 if |Yt−Yt−1|≤δ, where the threshold is computed from previous data by: δ=1.96∗√2∗σ. The parameters (μ,σ) can also be estimated from previous data. 3.4.3.3 Algorithm 3 (threshold of slope) The threshold of slope approach assumes that the slope at time t is s(t)=(Yt−Yt−1)/Δt; (t≥2), where Δt is the time interval. The experiment reaches plateau at t if the slope is less than a certain threshold: s(t)≤δ, where the dynamic threshold is based on only previous data under the same conditions: δ(t)=0.3∗1/(t−2)Σ(t−1,i=2)s(i). 126 The three algorithms were evaluated on a subset of the previously collected automated high-throughput reaction profiling data (Figures B.1.3 through B.1.5), identifying the plateau variability algorithm for further testing on the full dataset of automated high-throughput reaction profiles (Figure 3.16). Upon manually validating the plateau labeling of the full set of reaction profiles, it was determined that 170 out of 184 of plateaus were labeled correctly when compared to my “domain expert” labeling (the failures included 1 false negative plateau, 9 false positive plateaus and 3.1 time adjustments). This corresponded to a success rate of 92%. Thus, real time plateau detection algorithm 2 was implemented for use in subsequent autonomous optimizations. Figure 3. 16. Evaluation of the plateau variability algorithm on data from the first through fifth rounds of automated high-throughput reaction profiling. 127 3.4.4 Optimization algorithm selection The next step in building an autonomous optimizer for photochemical reactions was the definition of a sequential model based optimization strategy. Sequential model based optimization strategies rely upon the construction of a surrogate model of the objective function for the defined parameter space.65 The modeling is necessary because the objective function is typically not known, complex or difficult to sample in chemical process optimizations. Thus, a statistical surrogate model is constructed from a set of seed experiments, and an acquisition function is utilized to propose new experiments, the results of which feed into improving the surrogate model, from which the acquisition function proposes new experiments. This cycle is continued until a predefined output parameter threshold or number of experiments is reached (Figure 3.17). Here we discuss the selection of each step in the workflow, aside from the parameter space, which will be discussed in Section 3.5. Many of the decisions made relied upon the expertise of the MSD Biostatistics team and simulated optimization campaigns that were carried out on simulated experimental data based on the existing dataset of automated high-throughput reaction profiles (Figure B.1.7). The MSD Biostatistics team developed the optimization code, relying on open-source Python libraries.208 Figure 3. 17. Steps in a sequential model based optimization workflow. 128 The first decision point was the threshold for optimization. Here, 48 experiments were selected as the maximum threshold. The first eight of these experiments were designated as seed experiments, and the subsequent 40 were dedicated to optimization. With respect to the seed experiments, as opposed to random sampling of the parameter space, a D-optimal design was utilized to seed the parameter space more uniformly.209 The second decision point was the modeling strategy for constructing the statistical approximation of the objective function. Linear regression modeling was deemed a plausible approach, but so was Gaussian process modeling. Instead of opting for a single modeling method, a more pragmatic approach employing both modeling methods was selected. Also, if the model failed to predict a better experimental proposal, the selection strategy would revert to random sampling, allowing the model to be updated for improved predictions. The third decision point was the acquisition function. For linear regression modeling, the options included the predicted mean, prediction with standard error and upper prediction interval (UPR). Because each of these functions performed similarly in simulations, predicted mean was selected for the first autonomous experiment with linear regression modeling.210 For Bayesian optimization (BO) through Gaussian process modeling, the options to choose from included expected improvement (EI), probability of improvement (PI) and upper confidence bound (UCB).67,68 Again, because each of these functions performed similarly in simulations, expected improvement was selected for the first autonomous experiment. However, in later experiments, the selection was widened to include all three BO acquisition functions. 129 3.4.5 Integration of the closed-loop system The closed-loop integration was similar to that of the strategy implemented in Section 2.3.3., however the optimizer code was developed from scratch by the MSD Biostatistics group to accommodate a wider selection of open-source optimization strategies. In addition to this, the interface code that connected the optimizer code to the Chemspeed software was redeveloped by a coder in the MSD Data Rich Experimentation group to enable greater flexibility for redeployment to various chemical systems. The data flow is illustrated in Figure 3.18. The first step in the closed-loop system is the interface code configuration of the input parameters and ranges, input stock mixture concentrations, output parameters, UPLC retention times and retention time windows, and file locations. The optimizer code then suggests an experiment based on the input/output configuration. Once the optimizer code suggests an experiment, the interface code translates this to dispense volumes for experimental execution by the Chemspeed Autosuite software, which is configured to monitor the dispense volume file for new entries. Once the experimental execution is complete, a sample aliquot is injected into the sample valve. This is followed by a digital relay switch to trigger UPLC acquisition through the Agilent Chemstation software. Upon UPLC data processing in the Chemstation software, an auto-generated results file is parsed by the interface code, and the output relayed to the optimizer code, where the plateau detection algorithm determines whether or not a plateau has been reached. If a plateau has not been reached, the interface software triggers the Chemspeed Autosuite software aliquot another sample. If a plateau has been reached, the interface triggers the Chemspeed software to terminate sampling, and relays the final experimental results to the optimizer code, 130 which then suggest the next experiment through a pre-configured sequential-model based optimization algorithm (linear model based or Gaussian process model based BO). Figure 3. 18. Data flow in the integrated closed-loop optimization system. 3.5 Definition of the search space With the hardware and software requirements in place, the experimental search space was finalized (Table 3.1). The design of this search space was influenced by the results of high-throughput reaction monitoring and LED-NMR studies. The goal of the optimization was to uncover both local and global optima, and also understand parameter influences and interactions among them. A wide variety of categorical input parameters were selected, including bromination reagents (two options), acid additives (eight options) and solvents (two options). The bromination reagents and solvents were represented through integer encoding, while the acid additives were represented through molecular feature encoding, where the reported first pKa values in water were used.211 Despite our understanding that the reaction would be executed in organic solvents, we reasoned 131 that ranking the acids through their pKa measurements in water would still be an effective representation of relative acidity (Table 3.2). Based on our high-throughput reaction profiling studies we expected the optimal pKa window for rate acceleration to be in the -3 to 2 range. Three continuous parameters were included in the search space, including reagent equivalents, additive loading and reaction temperature. We hypothesized that reagent equivalents on the high end would lead to higher levels of dibromination, while additive loading and temperature would be positively correlated to rate acceleration. A discrete parameter was also included in the search pace, where five discrete light intensity stages were explored through autonomous control of the Lumidox II LED controller. The five light intensity stages corresponded to light intensity measurements of 60, 125, 190, 255 and 385 mW per LED. The inclusion of this parameter expanded the search space significantly from prior studies, which were limited to the lowest light intensity of 60 mW per well. Finally, the search space was focused on a single optimization output, the LC area % of monobrominated product 3.2, measured at 210 nm wavelength. Our goal was to maximize the amount of desired product 3.2 while minimizing the amounts of starting material 3.1 and side product 3.3. Prior scale-up experiments in flow revealed that the LC area % of product 3.2 correlated very well with the solution yield of product 3.2, thus, the analysis was limited to this output parameter measurement for simplicity. Further, there was concern around the possibility for an internal standard to react with the bromine radicals present in the reaction medium, ruling out the possibility of internal standard-based assay, and the sample volumes were far two low (5 µl) and imprecise for aliquot volume-based solution yield assay. Thus, LC area % proved to be the most reliable output parameter for this particular chemical system. 132 Table 3. 1. Input and output parameters for optimization. Input parameters Type Range Unit Reagent categorical NBS, DBDMH reagent Additive categorical 8 acids acid Solvent categorical ACN, DMC solvent Reagent equivalents continuous 1.0 – 1.5 molar equivalents Additive loading continuous 1 – 25 mol % Rxn temperature continuous 5 – 35 °C Light intensity stage discrete 1, 2, 3, 4, 5 stage Output parameters Type Objective Unit LC area % product 3.2 continuous Maximize LC area % at 210 nm Table 3. 2. Eight acids included in the APO listed in order of increasing first pKa (H2O). Acid pKa 1 (H2O)* Hydrochloric acid (HCl) -8.0 Sulfuric acid (H2SO4) -3.0 2-Picolinic acid 1.0 Phenylphosphonic acid (PPA) 1.9 Phosphoric acid (H3PO4) 2.1 DL-Lactic acid 3.9 Acetic acid (HOAc) 4.8 Water (H2O) 15.7 *Literature values.26 133 3.6 Reproducibility experiments Reproducibility experiments are useful in the development of autonomous systems because they have the potential to uncover both hardware and software issues, but more importantly, provide researchers with the confidence to trust self-optimization results. Our system posed an extra level of challenge in that a demonstration of consistent and reliable reaction profiles under constant reaction conditions were a requirement. Four reproducibility runs were carried out, and different hardware and software issues were resolved with each run until excellent reproducibility was observed in run 4 (Scheme 3.6, Table 3.3, and Figure 3.19). In the first run, the stock solution concentrations were high, which led to several to exist as solid liquid slurries. Although the wide bore Chemspeed needles, with their 0.8 mm inner diameters, are capable of slurry transfers, the accuracy and precision of the transfers suffered. Thus, in the first run, the reproducibility was extremely poor, where different reaction rates were observed in experiments carried out under the same conditions in two solvents, ACN and DMC. The stock mixture concentrations were gradually reduced until the compounds were fully dissolved, improving the process performance until excellent reproducibility was observed in runs 3 and 4. The reduction in stock solution concentrations resulted in a reduction of the reaction concentration, which increased the reaction rate under the same light intensity. Scheme 3. 6. Reaction conditions for reproducibility testing. 134 Table 3. 3. Stock solution concentrations for reproducibility testing. Run 1 Run 2 Run 3 Run 4 Scale in 4 63 µmol 25 µmol 20 µmol 20 µmol [3.1] stock 0.630 M 0.250 M 0.250 M 0.250 M [NBS] stock 0.945 M 0.375 M 0.250 M 0.250 M [H2SO4] stock 0.315 M 0.125 M 0.100 M 0.100 M [3.1] reaction 0.252 M 0.100 M 0.080 M 0.080 M Temperature 25 oC 25 oC 25 oC 5 oC Wavelength 470 nm 470 nm 470 nm 405 nm* *470 nm LED array that warped due to incompatibility with Syltherm at high temperate was replaced with a 405 nm LED array. Green: Slurry in ACN and DMC; Blue: slurry in DMC. Figure 3. 19. Time course monitoring of LC area % product 3.2 for reproducibility testing. 135 3.7 Autonomous optimization results With a demonstration of excellent reproducibility in hand, three autonomous process optimization (APO) campaigns were commenced. Each campaign began from the same set of eight D-optimal seed experiments, where one acid additive was explored in each experiment under different combinations of the remaining parameter points. No prior data from the high-throughput reaction monitoring experiments were included in the initial modeling, in order to reduce additional bias, as these experiments had already guided the design of the search space. Each campaign included between 34 and 48 sequential iterations that were executed and profiled over two minute irradiation intervals. The optimized output parameter was the final area % of product 3.2 upon reaching the reaction plateau. In order to maximize diversity, the optimizer code was configured to switch to random sampling mode once the model failed to predict outcomes that exceeded the maximum final area % of product 5. Once the model generated improved predictions in light of these random evaluations, the optimizer code was configured to switch back to optimization mode. The first optimization campaign explored the implementation of a linear regression (LM) model based sequential optimization strategy with a predicted mean acquisition function and was executed for 46 iterations (the time course data is reported in Figure 3.20). The second optimization campaign explored the implementation of a Gaussian process (GP) model based Bayesian optimization (BO) strategy with an expected improvement (EI) acquisition function and was executed for 34 iterations (the time course data is reported in Figure 3.21). The third optimization campaign explored the implementation of a Gaussian process (GP) model based Bayesian optimization strategy (BO) with three alternating acquisition functions, including expected 136 improvement (EI), probability of improvement (PI) and upper confidence bound (UCB). The final campaign was executed for 48 iterations (the time course data is reported in Figure 3.22). The time course data reveals three general categories of kinetic profiles. In the first kinetic profile, the reaction is slow and the plateau is reached at very low product levels (for example, plot 33 in Figure 3.20 and plot 26 in Figure 3.22). In the second kinetic profile, moderate and controlled rate acceleration is observed, resulting in moderate to high product formation (for example, plot 39 in Figure 3.20 and plot 28 in Figure 3.21). In the third kinetic profile, excellent rate acceleration is observed, but this acceleration is accompanied by rapid decomposition upon reaching a maximum product level (for example, plots 1, 5 and 19 in Figure 3.20). The ideal process would display the second kinetic profile, reaching a high level of product in a controlled fashion, with a low risk of decomposition. We observed that both BO campaigns converged to optima exhibiting the second kinetic profile category (Figures 3.21 and 3.22), while the LM campaign did not appear to converge at all, but this can be mostly attributed to the optimizer code focusing on random sampling past iteration 21 (Figure 3.20). The reactions in the second and third kinetic profile categories reached their plateaus within 24 minutes, and the plateau detection algorithm performed as expected. Moreover, monitoring the reaction plateau and reporting the reaction outcome at the plateau point resulted in a much more meaningful comparison among the varied process conditions. Take, for example, the cases of iteration 19 and 39 in the first optimization campaign (plots 19 and 39 in Figure 3.20). If both iterations were sampled at 4 minutes, it would have appeared that 19 outperformed 39. Instead, sampling both iterations at their plateau points (10 minutes for 19 and 12 minutes for 39) for 137 autonomous optimization revealed that, in fact, 39 significantly outperformed 19 upon reaction completion, because decomposition was observed in 19 after 4 minutes of reaction time. Figure 3. 20. Reaction profiles of the first campaign with a linear regression model (LM) based optimization sampled with the predicted mean acquisition function. D-optimal design seed in iterations 1-8; random sampling in iterations 22, 23, 24, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 42, 43, 44, 45, 46. Colored by iteration. Conditions: 20 µmol 3.1, 20 - 30 µmol bromination reagent, 0.2 - 5.0 µmol acid additive in solvent (0.06M) irradiated with 470 nm LEDs at intensity level 1 -5 at 5 - 35 °C. 138 Figure 3. 21. Reaction profiles of the second campaign with Gaussian process (GP) model-based Bayesian optimization (BO) sampled with the expected improvement (EI) acquisition function. D-optimal design seed in iterations 1-8. Colored by iteration. Conditions: 20 µmol 3.1, 20 - 30 µmol bromination reagent, 0.2 - 5.0 µmol acid additive in solvent (0.06M) irradiated with 470 nm LEDs at intensity level 1 -5 at 5 - 35 °C. 139 Figure 3. 22. Reaction profiles of the third campaign with Gaussian process (GP) model-based Bayesian optimization (BO) sampled with alternating expected improvement (EI), probability of improvement (PI), and upper confidence bound (UCB) acquisition functions. D-optimal design seed in iterations 1-8; random sampling in integration 10. Colored by iteration. Conditions: 20 µmol 3.1, 20 - 30 µmol bromination reagent, 0.2 - 5.0 µmol acid additive in solvent (0.06M) irradiated with 470 nm LEDs at intensity level 1 -5 at 5 - 35 °C. 140 Although the time course data presented in Figures 3.20 through 3.22 demonstrated the various kinetic profiles that could be observed in the photobromination reaction under study, additional visualizations that would provide deeper insights around local and global optima were still needed (Scheme 3.7). Given that three categorical parameters were under evaluation, including reagent, additive and solvent, we hypothesized that multiple global optima were likely to exist. This certainly made the optimization more challenging. For multivariate data, we found the combined bar chart, line and scatter plot format previously introduced in Section 2.4 to be the most informative data visualization technique (Figures 3.23 through 3.25). Scheme 3. 7. Photobromination reaction and process parameters under study. Visualization of the multivariate data from first LM based optimization campaign demonstrates the importance of reagent, solvent and additive selection (Figure 3.23). NBS outperformed DBDMH under a majority of conditions and DMC appeared to yield optimal results in combination with a broader selection of acid additives. The optimizer appeared to focus on HCl and H2SO4, which promote significant rate acceleration, however the kinetic profiles with these additives aligned with category 3 (decomposition after a maximum product level) at higher additive loadings. The data left us with questions around how well higher pKa additives might have performed if sampled more frequently. 141 Figure 3. 23. Visualization of the first campaign with a linear regression model (LM) based optimization sampled with the predicted mean acquisition function. D-optimal design seed in iterations 1-8; random sampling in iterations 22, 23, 24, 26, 27, 30, 31, 33, 34, 35, 36, 38, 39, 42, 43, 44, 45, 46. Grouped by reagent, solvent, additive and round. Sorted by additive loading. Colored by plateau time. 142 Visualization of the multivariate data from the second and third BO campaigns answered some of the questions that arose from the first campaign (Figures 3.24 and 3.25). The second campaign, which relied on BO with the EI acquisition function, also focused on HCl and H2SO4 with NBS in DMC, with similar observations around decomposition under high loadings of these two additives. Here, acetic acid with NBS in DMC was sampled with higher frequency, and it appeared to promote moderate rate acceleration to generate high product levels. The decomposition observed with the lower pKa acids at higher loadings did not appear to be an issue with this higher pKa acid. The third campaign, which relied on BO, and alternated the EI, PI and UCB acquisition functions, shifted the focus from the lower to higher pKa acids, such as phosphoric, lactic and acetic acid. This is likely because of the implementation of the UCB acquisition function, which was designed to explore unsampled regions of the parameter space, thus, it is not surprising that additional local optima were revealed with the implementation of this acquisition function (See Figure B.1.6 for a visualization of how each acquisition function sampled the search space in this campaign). Although lactic acid promoted moderate rate acceleration, the reactions were still quite fast, reaching their plateaus within 8 and 20 minutes. What is preferred about lactic acid, from a process chemistry perspective, is the robustness in product levels observed across the entire sampled range, with minimal decomposition risk. With these considerations in mind, we determined the optimum conditions for this process to be 1.5 equivalents NBS with 7 mol % lactic acid in DMC, at a light intensity stage of 1 (60 mW per well) at 15 oC for 16 minutes, resulting in the generation of 86 LC area % of product 3.2. It is important to note that we were unable to make these observations during high throughput reaction profiling, because lactic acid with NBS was only sampled in ACN, and the performance was found 143 to be inferior in this solvent system (Figure 3.4). In short, the algorithmic optimization of a multivariate parameter space in tandem allowed for a broader variety of parameter combinations to be explored, unveiling new local optima with kinetic profiles more amenable to large scale processing. Figure 3. 24. Visualization of the second campaign with Gaussian process (GP) model based Bayesian optimization (BO) sampled with the expected improvement (EI) acquisition function. D-optimal design seed in iterations 1-8. Grouped by reagent, solvent, additive and round. Sorted by additive loading. Colored by plateau time. 144 Figure 3. 25. Visualization of the third campaign with Gaussian process (GP) model-based Bayesian optimization (BO) sampled with alternating expected improvement (EI), probability of improvement (PI), and upper confidence bound (UCB) acquisition functions. D-optimal design seed in iterations 1-8; random sampling in integration 10. Grouped by reagent, solvent, additive and round. Sorted by additive loading. Colored by plateau time. 145 3.8 Assessment of optimization performance An important question to answer in researching optimization algorithms for chemical process optimization is how well the technology performs with respect to (1) arrival at a global optimum, and (2) gaining process insights. The parameter space in this optimization certainly proved to be challenging, with several local optima of similar performance as well as interactions among the various parameters. When we first embarked upon the execution of three optimization strategies, we were interested in identifying a universal strategy for the effective multivariate chemical process optimization of the photobromination reaction under study. Research is never that simple, and the answer that we found was quite different than anticipated. The answer was that the strength of our approach was the ability to combine a complementary set of optimization strategies. The combined data from all three optimization strategies provided the information to confidently determine the global optimum despite the presence of several local optima with NBS in DMC (Figure 3.26). To differentiate between the local optima, we turned to the concept of process of robustness to determine which acid additives displayed optimum behavior under the broadest range of process parameters. These additives appeared to be sulfuric and lactic acid. No single optimization campaign would be allowed for us to confidently arrive at this conclusion. Also, because each optimization strategy tended to converge at some point in the optimization, simply extending a single campaign to a larger number of iterations would not have been as effective as the combination of three complementary campaigns. 146 Figure 3. 26. Visualization of the results from three combined optimization campaigns. This gives the most comprehensive view of the process global optimum, the various local optima and process insights around additive loading. Grouped by reagent, solvent, additive and combined experiment number. Sorted by additive loading. Colored by plateau time. 147 Despite the fact that we were not able to pinpoint the best optimization strategy, comparisons of the three approaches did provide us with a better understanding around the influence of the model choice and sampling strategy on the optimization performance. Firstly, our linear model assumed a linear relationship between the additive loading and the product level at reaction plateau. This assumption was proven incorrect in the first optimization campaign, where we found that high loadings of low pKa acids resulted in low levels of product. This incorrect assumption was based on the high throughput reaction profiling data, but we had not explored as wide of a range of additive loadings in those experiments. Thus, even the most informed assumptions made in developing regression models may lead to poor optimization performance, if incorrect. Bayesian optimization, on the other hand, is based on GP model approximations of the seed data and thus is not tied to such assumptions. Therefore, BO through GP regression would be our first choice in future optimizations. The impact of the sampling strategy on optimization performance was also explored. Figure 3.27 illustrates box plots of the medians and interquartile ranges of product LC area % upon the implementation of different acquisition functions for sampling the parameter space. The literature indicated that PI would be the most exploitative acquisition function, sampling closer to the predicted optimum, and that UCB would be the most explorative acquisition function, sampling areas of high uncertainty.67,68 The data illustrated in the box plots support this literature precedent. For example, in campaign 3, where the EI, PI and UCB strategies were alternated in the same run, PI sampled areas that produced product levels close to the optimum (exploitation), while UCB sampled areas that produced a wider range of product levels (exploration). Intuitively, one might speculate that sampling closer to the predicted optimum to be more effective, but when faced with 148 objective functions with multiple local optima, some exploration is critical to determination of the global optimum and for gaining process insight. Figure 3. 27. Box plot of LC area % product under various sampling strategies. Image generated by Yuting Xu. Another way to compare the various sampling strategies was to compare the number of iterations dedicated to each additive using each acquisition function (Figure 3.28). As expected, in the third Bayesian optimization campaign, UCB sampled 7 out of 8 additives, while PI focused on 3, and EI sampled 5. In the second Bayesian optimization campaign, EI once again sampled 5 out of 8 additives. Finally, in the first LM based optimization campaign, mean best prediction sampled 5 out of 8 additives. 149 Figure 3. 28. Count of iterations per each additive under different sampling strategies. Finally, we were interested in understanding both the convergence behavior and sampling diversity of each optimization strategy. Our collaborators in the MSD Biostatistics group turned to multidimensional scaling (MDS) in order to flatten the combinatorial parameter space and more clearly observe the optimization trajectory (Figure 3.29).212 This allowed for (1) the establishment of evidence for optimization convergence and (2) the assessment of diversity in sampling the combinatorial parameter space. As mentioned previously, the goal of the optimization was to achieve a balance between a global optimum while gaining sufficient insight into the process robustness. Figure 3.29.a indicates that campaigns 2 and 3 converged, but campaign 1 did not. Figure 3.29.b indicates that the highest sampling diversity was observed in campaign 1, but the diversity was attributed to random sampling upon the inability of the linear regression model to make better predictions. The sampling diversity was moderately high in campaign 3 due to the implementation of three different acquisition functions. Interestingly, it appears that the optimizer 150 was going in circles at one point in the campaign 3 as it cycled between the three acquisition functions, thus, in future optimizations, it may be more effective to begin with UCB in order to increase the initial sampling diversity, then switch to EI in the second half of the run to facilitate convergence. Figure 3. 29. Multidimensional scaling (MDS) visualizations. a) The optimization trajectory in three campaigns, with the purple arrows illustrating the optimization path (from light to dark) and yellow points indicating the initial seed and random samples. b) The sampling diversity colored by the different acquisition functions implemented in each strategy. 151 3.9 Qualitative modeling to determine process parameter influences Although the visual analyses presented up to this stage provided an understanding around categorical parameters such as best reagent, best solvent and best additive(s), as well as some understanding around continuous parameter effects such as additive loading, the multivariate nature of the data made it difficult to further tease out individual continuous parameter effects. For this, we turned to Random Forest (RF) modeling, a machine learning technique that is especially effective in modeling categorical data.109 Once again, we enrolled the help of the MSD Biostatistics team to assist in qualitative modeling. When applied to the combined APO data, RF modeling was used to rank the influence of each parameter on the process outcome (Figure 3.30.a), as well as model the partial dependence of continuous parameters such as additive pKa (Figure 3.30.b) and additive loading (Figure 3.30.c) on predicted product 3.2 LC area %. The ranking of parameter impacts aligned very well with our qualitative observations, where reagent, solvent, additive pKa and additive loading were determined to be most critical, while light intensity stage and temperature were determined to be less critical. The minimal impact of light intensity on reaction performance is not surprising because the Wohl-Ziegler bromination is proposed to proceed through a radical chain mechanism. In radical reactions with low quantum yields, light intensity can be a critical optimization parameter and should be investigated.195 The partial dependence plots of additive pKa and loading on predicted product 3.2 LC area % also aligned very well with our qualitative observations, where optimal performance with lower pKa additives was observed at lower loadings. 152 Figure 3. 30. Random forest (RF) model of the combined autonomous optimization data. a) Ranking of parameter influences b) Partial dependence of predicted product 3.2 LC area % on additive pKa c) Partial dependence of predicted product 3.2 LC area % on additive loading. Image generated by Yuting Xu. As multivariate optimization data sets become more complex, methods such as RF modeling will become critical tools for the interpretation of APO data. We are confident that RF modeling is a reliable tool for interpreting this data set because of the excellent alignment we found with our qualitative observations, but also caution that the model is only as good as the training data, and that extrapolation to parameter choices that fall outside of the training set are not reliable. In addition, model overfitting is always a concern that must be considered. 153 3.10 Conclusion In conclusion, we developed an autonomous process optimization (APO) system for the optimization of photochemical processes in batch microplate reactors with reaction monitoring and plateau detection capabilities. We applied this system to the optimization of a Wohl-Ziegler photobromination process. Even with a complex input parameter space including three categorical and three continuous parameters along with one discrete parameter, our optimization approach uncovered multiple local optima, determined a global optimum, and also gain process insights around the impacts of additive pKa and additive loading. The reaction monitoring capability allowed for the identification of three kinetic profiles associated with the process, which were largely influenced by reagent, solvent, and additive, as well as additive pKa and loading. The plateau detection capability allowed for reporting the process outcomes at the true reaction end points, capturing the true impact of degradation pathways and side product levels. The exploration of a variety of optimization strategies not only supported the implementation of complementary strategies for future optimization challenges, but also unveiled the pros and cons of each strategy, both in theory and in practice. Finally, the implementation of various data visualization techniques and random forest (RF) modeling allowed the unveiling of process insights. Future optimizations around this chemistry will be more successful through further automation of the decision-making process by developing multiobjective functions for measuring product levels, degradation and robustness in tandem. 154 3.11 Methods 3.11.1 General Commercial reagents were purchased from Millipore Sigma, Oakwood, Combi-Blocks, Alfa Aesar, Acros, and Strem, and used as received. Anhydrous solvents were purchased from Millipore Sigma and used as received. Aqueous solutions were deoxygenated through subsurface nitrogen sparge for a minimum of 30 minutes. Stock solutions were prepared manually in anhydrous solvents under N2 atmosphere and placed on the robot deck for autonomous execution. A fluoropolymer and metal top-sealed 48-well photoreactor with 1 mL glass vial inserts was equilibrated at the designated reaction temperature under 20 psig of N2 with 400 rpm agitation. The photoreactor was irradiated with an LED array. 3.11.2 Equipment Autonomous optimization experiments were executed using a Chemspeed SWING XL robotic system equipped with a four-needle dispense head and four 1 mL syringe pumps to enable accurate dispenses at low volumes. Agitation was carried out through an integrated custom V&P scientific two-position tumble stirring module and temperature control was achieved through an integrated Huber Unistat chiller with temperature feedback control. Photochemical reactions were executed in a 48 well Analytical Sales Temperature Controlled Reactor (TCR) prototype and irradiated with a modified Analytical Sales Lumidox II LED array. Online HPLC analysis was carried out through an integrated Agilent 1290 UPLC equipped with a photodiode array detector and a custom sampling valve installed on the robot. 155 Compounds were purified on a CombiFlash purification system employing Redisep Rf Gold silica gel columns. 1H NMR spectra for characterization were acquired on a Bruker 500 MHz instrument (4 scans). Samples were prepared in DMSO-d6 and spectra were calibrated to the DMSO-d6 reference peak at 2.50 ppm. 13C NMR spectra for characterization were recorded on a Bruker 500 MHz instrument (5120 scans). Samples were prepared in DMSO-d6 and spectra were calibrated to the DMSO-d6 reference peak at 40 ppm. ESI+ MS for characterization were acquired on an Agilent 1290 UPLC-MS. 3.11.3 Procedures 3.11.3.1 Representative procedure for high throughput reaction profiling experiments The described procedure was executed in batch for 48 - 96 reactions. To each well was dispensed 200 µl of a mixture of starting material 3.1 (63.0 µmol, 0.315 M), brominating reagent (75.6 µmol, 0.378 M), and octofluorotoluene (6.3 µmol, 0.032 M). This was followed by the addition of acid additive (6.3 µmol, 50 µl of 0.126 M stock solution). The reaction mixture was irradiated with a 405 or 470 nm LED array for the designated time interval at 25 oC. The LED array was switched off and 10 µl of each reaction mixture was transferred to a 96-well polypropylene collection block prefilled with 800 µl of ACN diluent. The irradiation-sampling sequence was continued until between 6 and 12 samples were aliquoted and diluted from each reaction mixture. The diluted samples were subjected to offline UPLC analysis. 3.11.3.2 Representative procedure for LED NMR experiments To a 2 mL volumetric flask under N2 atmosphere was dispensed stock solutions of 6-methyl-4-(trifluoromethyl)pyridazin-3(2H)-one 3.1 (25.0 mg, 0.140 mmol) and NBS (30.3 mg, 0.168 mmol) 156 in ACN-d3. This was followed by the addition of a stock solution of H3PO4 (1.39 mg, 0.0140 mmol) in ACN-d3. The resulting mixture was adjusted to 2 mL of volume with ACN-d3 and 600 µl was transferred to a 5 mm thin wall NMR tube followed by the placement of the coaxial insert. The joint was parafilmed and the sample was wrapped with foil to keep out the light. The sample was irradiated at 445 nm at the desired temperature under LED NMR acquisition. 3.11.3.3 Procedure for scale-up experiment in flow To a 50 mL volumetric flask was added 6-methyl-4-(trifluoromethyl)pyridazin-3(2H)-one 3.1 (12.5 mmol, 2.23 g) and NBS (15.0 mmol, 2.67 g), followed by 25 mL of ACN to dissolve all solids. H2SO4 (1.25 mmol, 0.0668 mL) was added and the volume adjusted to 50 mL with ACN. The solution was flowed through a Vaportec system equipped with a 3 mL reactor under 450 nm irradiation at 0.5 ml/min (6 min residence time). After 25 min, the product stream collection was initiated and continued for 65 min. 32.6 ml was collected to generate 6-(bromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.2 in 69% assay yield and 6-(dibromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.3 in 13% assay yield, with 5% assay yield 6-methyl-4-(trifluoromethyl)pyridazin-3(2H)-one 3.1 remaining. 3.11.3.4 Representative procedure for autonomous reproducibility experiments The described procedure was executed sequentially for each reaction. To each well was dispensed methyl pyridazinone 3.1 (20 µmol, 80 µl of 0.25 M stock solution), NBS (25 µmol, 100 µl of 0.25 M stock solution), H2SO4 (2 µmol, 20 µl of 0.10 M stock solution), and anhydrous ACN or DMC (50 µl) to ensure a total reaction volume of 250 µl. The reaction mixture was irradiated with an LED array for two minutes at 25 oC. The LED array was switched off and 5 µl of the reaction 157 mixture was transferred to a 96-well polypropylene collection block prefilled with 400 µl of ACN diluent. Upon needle-mixing, 40 ul of diluted sample from the collection block was aliquoted and injected to the on-deck sampling valve outfitted with a 2 µl loop. The valve was automatically switched to transfer the sample to the UPLC for analysis. The irradiation-sampling-online analysis sequence was continued until the reaction reached a plateau or a maximum number of 12 samples were analyzed. 3.11.3.5 Representative procedure for autonomous optimization reactions The described procedure was executed sequentially for each reaction. To each well was dispensed starting material 3.1 (20 µmol, 80 µl of 0.25 M stock solution), brominating reagent (20 - 30 µmol, 80 - 120 µl of 0.25 M stock solution), acid additive (0.2 – 5.0 µmol, 2 - 50 µl of 0.10 M stock solution), and anhydrous ACN or DMC (100 - 188 µl) to ensure a total reaction volume of 350 µl. The reaction mixture was irradiated with an LED array for two minutes at a designated reaction temperature between 5 and 35 oC. The LED array was switched off and 5 µl of the reaction mixture was transferred to a 96-well polypropylene collection block prefilled with 400 µl of ACN diluent. Upon needle-mixing, 40 ul of diluted sample from the collection block was aliquoted and injected to the on-deck sampling valve outfitted with a 2 µl loop. The valve was automatically switched to transfer the sample to the UPLC for analysis (Figure 3.31). The irradiation-sampling-online analysis sequence was continued until the reaction reached a plateau or a maximum number of 12 samples were analyzed. 158 Figure 3. 31. Image of the Chemspeed SWING XL deck for autonomous experimentation. 3.11.3.6 Procedures for the synthesis of noncommercial compounds 6-methyl-4-(trifluoromethyl)pyridazin-3(2H)-one 3.1: To a 1 L flask was added acetone (1.92 mol, 112 g, 141 ml) and AcOH (400 mL). Methyl 3,3,3-trifluoro-2-oxopropanoate 3.13 (1.28 mol, 200 g, 145 ml) was charged via addition funnel over 2 min, raising the temperature rose to 46 oC. The resulting mixture was flowed through a 5 ml flow reactor housed in a 150 oC oven until steady state was established at 0.416 ml/min and the reactor output was collected for 18 h 30 min to yield 519 g of solution containing 192 g (0.897 mol) of methyl 2-hydroxy-4-oxo-2-(trifluoromethyl)pentanoate 3.14. 159 To a 3 L round bottom flask was transferred 290 g of solution containing 107 g (0.500 mol) of methyl 2-hydroxy-4-oxo-2-(trifluoromethyl)pentanoate 3.14. To this solution was charged hydrazine hydrate (0.752 mol, 37.6 g, 36.5 ml) under N2 atmosphere over 10 min, raising the temperature to 50 oC. The reaction was aged at 80 oC for 1 h. The reaction was cooled to 25 oC and 322 ml of water was added via addition funnel over 20 min. This was followed by the addition of 644 ml 35 wt% NaH2PO4 via addition funnel over 40 min. The mixture was cooled to 0 oC, filtered and the cake washed twice with 160 ml water. The wet cake was dried in a vacuum oven under N2 sweep to yield 77.3 g of 6-methyl-4-(trifluoromethyl)pyridazin-3(2H)-one 3.1 in 87% yield. 1H NMR (500 MHz, DMSO-d6) δ 13.39 (s, 1H), 7.86 – 7.83 (m, 1H), 2.31 (s, 3H). 13C NMR (126 MHz, DMSO-d6) δ 156.42, 144.42, 133.99 – 133.63 (m), 127.35 (q, J = 31.0 Hz), 122.09 (q, J = 272.7 Hz), 20.52. MS ESI+: (m/z calc. for C6H5F3N2O, [M + H] = 179.0); found = 179.0. 6-(bromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.2: To a 100 mL round bottom flask was added 6-(hydroxymethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.15 (25.8 mmol, 5.00 g), CBr4 (38.6 mmol, 12.8 g) and ACN (50 ml). The mixture was cooled in an ice batch and PPh3 (28.3 mmol, 7.42 g) was added, raising the temperature to 21 160 oC. The mixture was concentrated to dryness and purified over silica gel to yield 6-(bromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.2 (the yield was not determined). 1H NMR (500 MHz, DMSO-d6) δ 13.78 (s, 1H), 8.09 – 8.05 (m, 1H), 4.62 (s, 2H). 13C NMR (126 MHz, DMSO-d6) δ 156.26, 143.97, 133.10 (q, J = 4.9 Hz), 128.16 (q, J = 31.5 Hz), 121.80 (q, J = 272.9 Hz), 31.40. MS ESI+: (m/z calc. for C6H4BrF3N2O, [M + H] = 256.9); found = 256.9. 6-(dibromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.3: To a 40 ml vial was charged 6-methyl-4-(trifluoromethyl)pyridazin-3(2H)-one 3.1 (22.5 mmol, 4.00 g), NBS (56.1 mmol, 10.0 g) and ACN (40 ml). This was followed by the addition of H2SO4 (2.25 mmol, 0.220 g, 0.120 ml). The mixture was split into 2 vials and irradiated with a Penn PhD photoreactor at 470 nm for 4 h. The mixture was concentrated to dryness and purified over silica gel to yield 5.56 g of 6-(dibromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.3 in 82% yield. 1H NMR (500 MHz, DMSO-d6) δ 13.92 (s, 1H), 8.15 – 8.11 (m, 1H), 7.25 (s, 1H). 13C NMR (126 MHz, DMSO-d6) δ 156.12, 144.94, 130.57 (q, J = 4.9 Hz), 128.76 (q, J = 31.8 Hz), 121.52 (q, J = 273.1 Hz), 38.27. MS ESI+: (m/z calc. for C6H3Br2F3N2O, [M + H] = 334.9); found = 334.9. 161 Chapter 4: Conclusion Autonomous chemical process optimization is an emerging field, with the first reports of the technology emerging only a decade ago. When we first embarked upon this research, the field was limited to execution of APO in custom-built flow reactors and most studies required large quantities of material to reach steady state conditions. Execution was typically sequential, limiting the technology to fast reactions. In order to expand the technology to a wider array of chemical processes designed for batch reactors, we took the pragmatic approach of connecting two commercial automation systems equipped with microscale batch reactors with online analytics and open source optimization algorithms. By doing so, we equipped these commercial automation systems with artificial intelligence, allowing the execution of parallel experiments with little to no human intervention. Although this technology development required significant time investment, the benefits became immediately apparent when the SARS-CoV-2 pandemic hit and it became necessary to carry out several of the studies reported here remotely. A key feature of this technology is that integration is achieved through file parsing rather than a complicated application programming interface (API). This feature renders the technology modular, enabling extension to a variety of automation equipment such as benchtop parallel reactors, analytical instrumentation such as flow NMR or FTIR, and a variety of open source optimization algorithms (Figure 4.1). We envision a not so distant future filled with intelligent robotic systems capable of executing data-driven actions through autonomous feedback loops. Because chemical procedures are rooted in actions based on observations, feedback loops will improve the automation of chemical processes. 162 Figure 4. 1. The APO approach can be extended to a variety of automation equipment What are the benefits of autonomous process optimization? We already discussed the efficiency gain to scientific researchers through automation of mundane and repetitive optimization tasks typically executed by changing one variable at a time (OVAT). Optimization algorithms, on the other hand, are capable of executing the optimization of highly complex, multivariate searches through sampling only a subset of the parameter space. Take, for example, the two projects described in detail in Chapters 2 and 3. The search space that we identified for the optimization of a stereoselective Suzuki coupling reaction included 23 categorical ligand choices in combination with three continuous parameter ranges. If we were to explore three levels (low, medium and high) for each continuous parameter range with each ligand, we would need to explore 621 parameter point combinations. Bayesian optimization achieved the optimization of this search space in a small subset of 192 experiments, resulting in conditions that yielded product 2.3 in 73% yield. The search space that we identified for the optimization of a photobromination reaction included eight 163 categorical additive choices in combination with three continuous parameter ranges and one discrete parameter range. If we were to explore three levels (low, medium and high) for each continuous and discrete parameter with each additive, we would need to explore 648 parameter point combinations. A combination of sequential model based optimization approaches achieved the optimization of this search space in a much smaller subset of 128 experiments, resulting in conditions that generated product 3.2 in 86 LC area % purity. We are not aware of a single scientific researcher interested in executing over 100 experiments just to determine an optimum set of conditions, even if the experiments are automated. An equally important objective is to gain process insights from the data generated. Because sequential model based optimization algorithms change multiple variables in every iteration, the data can be difficult to interpret, but we have identified methods that allow the extraction of key parameter influences through random forest (RF) modeling and qualitative data visualization. In both projects, we gained a number of significant process insights, such as the preference for stereoretention under Pd-triarylphosphine catalysis and the influence of the phosphine to palladium ratio on the stereochemical outcome. In the photobromination reaction, we were able to qualitatively assess process robustness across the continuous and discrete parameter space for each ligand. Importantly, in both projects, we observed that each categorical parameter displayed optimal performance under a different combination of continuous and discrete parameter points, meaning that if we were to explore these categorical parameters under a fixed set of process conditions, we would have missed the global optimum. For example, in the photobromination chemistry, we observed that acetic acid and water were viable rate accelerants in DMC, but not in ACN. In fact, 164 we missed the potential for these additives during high throughput reaction profiling studies because the initial screens were limited to ACN. Because algorithmic optimization possesses the novel capability to navigate the search space through a unique path, unexpected insights can arise. For example, upon executing 182 high throughput reaction profiling studies on the photobromination process prior to algorithmic optimization, the data showed a positive correlation between additive loading and product formation. The algorithmic optimization data proved otherwise, where high additive loadings led to decomposition in the presence of low pKa acids, instead, loadings in the lower end of the range afforded optimal performance. Definition of the input parameter space is of critical importance in algorithmic optimization. The unbiased selection of categorical input parameters led to more successful optimization of the Suzuki coupling reaction described in Chapter 2. This was achieved by encoding phosphine ligand structures with 4D quantum mechanical features and leveraging unsupervised machine learning, which learns patterns from these untagged features, to map the ligand chemical space for diverse candidate selection. In addition, the diverse selection of acid additives through their first pKa measurements allowed for a diverse set of acid strengths to be sampled in the optimization of the photobromination reaction described in Chapter 3. Definition of the output parameter space is also of critical importance in algorithmic optimization. A consideration as simple as sampling time can play a critical role in optimization performance. The Suzuki coupling reaction described in Chapter 2 typically completed in one hour under the 165 process conditions studied in reaction profiling experiments. Thus, the robotic protocol was configured to sample each optimization iteration at the two hour time point under the assumption that a majority of the reactions would be complete within that time frame. This was a completely acceptable choice for this particular optimization, but the photobromination reaction described in Chapter 3 required a different choice. This particular reaction can be very fast in the presence of catalytic acid, with reaction completion times between 4 and 24 minutes. The issue was that the formation of dibrominated impurity 3.3, the leading cause of low product purity, is accelerated towards the end of the reaction as the product concentration increases. Thus, in order to effectively measure the purity of product 3.2, it is important to sample each iteration as close to the reaction end point as possible. Here, reaction monitoring and plateau detection was implemented to dynamically determine the reaction end point for output purity reporting. In addition to this, reaction profiling led to the finding that certain process conditions caused significant product decomposition after reaction completion, an important finding that would have been missed without timecourse data. There are of course limitations to autonomous process optimization. If the search space is biased and not broad enough, then the optimization performance will reflect that. Also, although it is possible to gain significant process insights from algorithmic optimization data, one must bear in mind that the optimizer has the potential to bias towards a local optimum if not configured for enough exploration, and the global optimum may be missed. This can be circumvented by the application of multiple optimization strategies that complement one another. 166 A significant drawback of autonomous optimization is the cost and effort associated with the development of integrated closed-loop systems consisting of optimization algorithms, robotic platforms and analytical instruments. This drawback is likely the major barrier to the widespread adoption of closed-loop autonomous systems. In order to overcome this barrier, the availability of low-cost modular robotic platforms and analytical instrumentation must increase and their operational, training and maintenance aspects must be simplified. In addition, there is a clear driver for expanding the graduate chemistry curriculum to include computational topics such as cheminformatics, coding, applied machine learning and optimization. The good news is that algorithmic process optimization does not have to be executed by robots, and human in the loop optimization is always a more accessible possibility. For this, we envision the development of web applications for chemists with interest in the application of optimization algorithms in the absence of coding experience. Under this scenario, experiments suggested by the optimization algorithm are executed manually, and the data is manually uploaded to the web application to drive subsequent rounds of experimental designs. The future of autonomous process optimization is bright and we are just at the edge of adopting this new technology in the chemical sciences. 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Definition of Sterimol features. a) Sterimol L value is the extension of the ligand along the axis of the bond connecting the phosphorus atom to the dummy metal atom. b) B1 and B5 are the minimum and maximum ligand width measured from the L axis.213–215 Reprinted with permission from reference 54.55 Copyright 2022 American Chemical Society. 184 A.2 Spectral data for compounds 2.1, 2.3, 2.4 and L20. Figure A.3. 1. MS ESI+ of 2.1. Figure A.3. 2. MS ESI+ of 2.3. Figure A.3. 3. MS ESI+ of 2.4. 185 Figure A.3. 4. 1H and 13C NMR spectra of 2.1 in DMSO-d6. 186 Figure A.3. 5. 1H and 13C NMR spectra of 2.3 in DMSO-d6. 187 Figure A.3. 6. 1H and 13C NMR spectra of 2.4 in DMSO-d6. 188 Figure A.3. 7. 1H and 31P NMR spectra of L20 in CDCl3. 189 A.3 Analytical methods Reaction analyses were carried out using an Agilent 1100 HPLC-UV using the method below: Table A.4. 1. HPLC-UV method for yield determination Column: Cortecs C18 2.7 µm, 4.6 x 150 mm Column Temperature: 55 oC Flow Rate: 1.5 ml/min Detection: 210 nm Acquisition Time: 10 min Mobile Phase: Solvent A = 2 mM ammonium formate in water; Solvent B = 2 mM ammonium formate in acetonitrile 10% water Mobile Phase Program: Time 0.00 min 6.00 min 8.00 min 8.10 min 10.00 min B% 5 95 95 5 5 Injection Volume: 5 µL 190 Compound Name: Retention time: 3-(benzyloxy)phenyl)boronic acid (2.2) 4.75 min 1,3,5-trimethoxybenzene 4.93 min (E)-Methyl 3-cyclopropyl-2-methyl-3-(tosyloxy)acrylate (2.1) 6.13 min (Z)-Methyl 3-(3-(benzyloxy)phenyl)-3-cyclopropyl-2-methylacrylate (2.4) 6.98 min (E)-Methyl 3-(3-(benzyloxy)phenyl)-3-cyclopropyl-2-methylacrylate (2.3) 7.10 min Example Chromatogram 191 A.4 Calibration curves Alkenyl sulfonate 2.1, coupled product 2.3, and coupled product 2.4 were assayed for weight % purity by 1H NMR against an internal standard of known weight and purity (99% 1,3,5-trimethoxybenzene). 1H NMR spectra for weight % purity determinations were recorded on a Bruker 500 MHz instrument with a delay time of 20 sec (32 scans). Samples were prepared in CDCl3 and spectra were calibrated to the CDCl3 reference peak at 7.26 ppm. The tables below show the weight % purity calculations using the equation: Puritycpd= Puritystd*Areacpd/Areastd*Protonsstd/Protonscpd*MWcpd/MWstd*Weightstd/Weightcpd. Table A.5. 1. Weight % purity calculation for 2.1 Peak Area (counts) Protons (#) MW (g/mol) Weight (mg) Purity (wt%) Internal Std. 5.30 3 168.19 19.75 99% 2.1 2.02 2 310.36 21.41 96% Table A.5. 2. Weight % purity calculation for 2.3 Peak Area (counts) Protons (#) MW (g/mol) Weight (mg) Purity (wt%) Internal Std. 5.35 3 168.19 19.83 99% 2.3 2.03 2 322.40 22.75 94% 192 Table A.5. 3. Weight % purity calculation for 2.4 Peak Area (counts) Protons (#) MW (g/mol) Weight (mg) Purity (wt%) Internal Std. 5.26 3 168.19 17.90 99% 2.4 1.96 2 322.40 19.55 97% These purities were used in the preparation of calibration curves for HPLC-UV assay to determine the mol % distribution of reaction components 2.1, 2.3 and 2.4 through measuring the ratio of their peak areas to an internal standard (IS) peak area (10 mol% 1,3,5-trimethoxybenzene). Four mixtures were prepared containing 10 mol% IS and 100, 75, 50 or 25 mol% of each compound. Each mixture was acquired on the Chemspeed online HPLC-UV system at 210 nm in duplicate. The mol% of compound in each mixture was plotted against the measured peak area ratio of each compound to IS, generating the calibration curves provided in figure A.5.1. Linear regression with the intercept set at zero allowed for the determination of response factors for straightforward conversion of compound to IS ratio to mol % compound through the following equation: mol % compound = m * peak area of compound at 210 nm / peak area of IS at 210 nm, where m = the response factor determined through calibration. These response factors were included in the Python script for automated calculation of mol % compound from the HPLC data. 193 Figure A.5. 1. Calibration curves for conversion of compound (2.1, 2.3, 2.4) to internal standard (IS) HPLC area ratios at 210 nm to mol % distribution in the reaction mixture. y = 39.344xR² = 0.99890204060801000.00 1.00 2.00 3.00mol% 1-EArea 1-E/ Area IS at 210 nmCalibration curve for 2.1AY%Linear (AY%)y = 14.672xR² = 10204060801000.00 2.00 4.00 6.00 8.00mol% 2-EArea 2-E/ Area IS at 210 nmCalibration curve for 2.3AY%Linear (AY%)y = 13.489xR² = 10204060801000.00 2.00 4.00 6.00 8.00mol% 2-ZArea 2-Z/ Area IS at 210 nmCalibration curve for 2.4AY%Linear (AY%) 194 A.5 Chemspeed protocol Below is an excerpt of the Chemspeed protocol used in autonomous optimization. 195 Figure A.6. 1. Chemspeed protocol for autonomous optimization of the Suzuki reaction. 196 A.6 Phosphine feature computations and training set selection Detailed electronic and steric descriptor calculations for 365 commercially available phosphine ligands have been disclosed previously.55 The main steps are: - conformational sampling of each ligand using Crest at the GFN2-xTB level in two model states: free ligand, [LNi(CO)3] complex.216,217 - selection of a representative set of conformers based on steric properties as relative energies. - DFT computations for the selected conformers in Gaussian 16 rev C.01: o geometry optimization and frequency calculation at the PBE-D3(BJ)/6-31+G(d) level. o single points for electronic properties at the PBE0-D3(BJ)/def2-TZVP level, including a population analysis with NBO 7.0.7, SMD solvation single point with the preset parameters for CHCl3, and single points for the radical cation and anion. - reading steric and electronic properties from the DFT computations. The following descriptors (118 total) were used in the subsequent analysis. Table A.6. 1. Boltzmann-weighted average descriptors descriptor brief description vmin_vmin Vmin = minimum of molecular electrostatic potential in P-lone pair vmin_r distance of Vmin from P fmo_e_homo HOMO energy fmo_e_lumo LUMO energy 197 fmo_mu chemical potential = average of HOMO/LUMO energies fmo_eta softness = difference of HOMO/LUMO energies fmo_omega Global electrophilicity parameter ω nbo_P Phosphorus natural population (NBO) nmr_P isotropic chemical shielding constant P nmrtens_sxx_P anisotropic shielding tensor XX-eigenvalue P nmrtens_syy_P anisotropic shielding tensor YY-eigenvalue P nmrtens_szz_P anisotropic shielding tensor ZZ-eigenvalue P efg_amp_P Electric field gradient tensor amplitude P efgtens_xx_P Electric field gradient tensor XX-eigenvalue P efgtens_yy_P Electric field gradient tensor YY-eigenvalue P efgtens_zz_P Electric field gradient tensor ZZ-eigenvalue P nuesp_P Electrostatic potential at P nbo_lp_P_percent_s s-orbital percentage of P lone pair (NBO) nbo_lp_P_occ P lone pair occupancy (NBO) nbo_lp_P_e P lone pair energy (NBO) nbo_bd_e_max P-C bonding orbital energy, highest (NBO) nbo_bd_e_avg P-C bonding orbital energy, average (NBO) nbo_bds_e_min P-C antibonding orbital energy, lowest (NBO) nbo_bds_e_avg P-C antibonding orbital energy, average (NBO) nbo_bd_occ_min P-C bonding orbital occupancy, lowest (NBO) nbo_bd_occ_avg P-C bonding orbital occupancy, average (NBO) 198 nbo_bds_occ_max P-C antibonding orbital occupancy, highest (NBO) nbo_bds_occ_avg P-C antibonding orbital occupancy, average (NBO) E_solv_total Energy of solvation E_solv_cds Electrostatic component of energy of solvation E_solv_elstat Non-electrostatic component of energy of solvation fukui_m Atom-condensed Fukui index f– fukui_p Atom-condensed Fukui index f+ vbur_ratio_vbur_vtot Ratio buried-to-total volume Boltzmann-weighted average (suffix _boltz) Table A.6. 2. Condensed descriptors descriptor brief description dipolemoment Dipole moment qpole_amp Quadrupole moment amplitude qpoletens_xx Quadrupole moment tensor XX component qpoletens_yy Quadrupole moment tensor YY component qpoletens_zz Quadrupole moment tensor ZZ component pyr_P Pyramidalization pyr_alpha Pyramidalization angle alpha vbur_vbur buried volume vbur_vtot total volume vbur_qvbur_min buried quadrant volume, lowest 199 vbur_qvbur_max buried quadrant volume, highest vbur_qvtot_min total quadrant volume, lowest vbur_qvtot_max total quadrant volume, highest vbur_max_delta_qvbur largest volume difference of neighborisc quadrants, buried vbur_max_delta_qvtot largest volume difference of neighborisc quadrants, total sterimol_B1 Sterimol B1 sterimol_B5 Sterimol B5 sterimol_L Sterimol L sterimol_burB1 buried Sterimol B1 sterimol_burB5 buried Sterimol B5 sterimol_burL buried Sterimol L Boltzmann-weighted average (_boltz), minimum (_min) and maximum (_max) across all conformers, difference between the conformer minimum and maximum (_delta) The 118-dimensional descriptor space was transformed using principal component analysis in scikit-learn, retaining the first 4 principal components. The resulting 4-dimensional space was divided into 24 regions by k-means clustering.58 Candidates for the training set were considered by increasing distance to each cluster center, taking practical considerations such as availability of the candidates into account. 200 A.7 Predictive modeling Suggestions for E-selective ligands were obtained by two different approaches: - regression modeling - classification modeling Experimental data from campaigns 2 and 3 were used for this procedure. In order to account for the subsequent changes to various reaction parameters during the autonomous optimization runs, the natural log of the highest yield of 2.3 that was obtained with each ligand under any reaction conditions in all experiments was used as optimization target. This is a practical approximation that is limited by the number of experiments that each ligand was used in and how optimal the other reaction conditions were for each ligand in any experiment. A.7.1. Regression modeling A model ensemble was generated consisting of three sparse linear models (four features each), identified by forward stepwise feature selection as described previously.170 After feature selection, each model was refit using ridge regression, automatically selecting alpha using internal cross-validation (RidgeCV in scikit-learn). The three models were chosen by diversity of the features and their validation statistics from a 75:25 training:test split determined by the Kennard-Stone algorithm based on the ligand descriptors. The models were then applied to the descriptors of ca. 900 P-based ligands to obtain predicted max(yield 2.3) of each model. Ligand candidates were selected based on the variance and average of predictions of each model in the ensemble, as well as practical considerations. Sometimes that entailed changing to a similar but commercially available ligand. 201 A.7.2. Classification modeling Candidates were selected by their similarity to the most selective ligand (PhSPhos) in the reduced chemical space representation as described previously.58 This procedure entails: - assign ligands to the classes most selective (PhSPhos) and less selective (all others) based on their experimental results. - train k-nearest neighbors (k = 1) classifier on the 4-dimensional PC space (see above) and apply that classifier to ca. 900 other P-based ligands for an estimate of the “most selective” region in this chemical space representation. - select ligand candidates that were classified “most selective” based on availability. A.7.3. Prediction results Scheme A.7. 1. Selected ligands. Ligands L36 and L38 were selected as commercially available alternatives to the related compounds shown below. The diazaphosphinane predictions were higher average (83% 2.3) and lower variance (0.136) than the commercially available ligand L36. The regression predictions for the ligands proposed by classification are shown for comparison. The model predictions for both 202 furyl phosphines had high variances, normally precluding the ligands as candidates, but the simple structure of L38 warranted further investigation nonetheless. Scheme A.7. 2. Compounds related to selected ligands. The two ligands with the biggest discrepancy between prediction and experimental results were L37 and L38. The training data did not contain information for heteroatom-substituted phosphorus compounds such as the phosphoramidite L37, so a poor prediction is not surprising. As discussed above, L38 showed a large variance between the predictions, indicating an unsafe prediction that would not normally be taken into consideration, so a large discrepancy is also not unexpected. Table A.7. 1. Comparison of predicted vs. experimentally determined yields of product 2.3 name source %2.3 pred, mean ln(%2.3) pred, variance %2.3, exp L33 Ph-PhenCarPhos Classification 22 0.001 39 L34 cataCXiumPInCy Classification 13 0.039 13 L35 PhDBP Regression 74 0.136 52 L36 PhP(NEt2)2 R.+Similarity 60 0.241 53 L37 (R)-MonoPhos Regression 68 0.040 12 L38 P(2-furyl)3 R.+Similarity 62 0.270 23 203 Appendix B - Chapter 3 B.1 Supplementary Figures Figure B.1. 1. Results with DBDMH in the first round of automated high-throughput reaction profiling studies. LC area % of starting material 3.1 (blue), product 3.2 (green) and side product 3.3 (red) profiled over 9 hours in 1.5 hour time intervals. Conditions: 63 µmol 3.1, 69.3 umol DBDMH and 6.3 µmol acid additive in ACN (0.25M) irradiated with 405 nm LEDs at 60 mW (level 1) intensity for 9 h. 204 Figure B.1. 2. Results in AcOH in the third round of automated high-throughput reaction profiling. LC area % of starting material 3.1 (blue), product 3.2 (green) and side product 3.3 (red) measured over 65 min in 5 min time intervals. Conditions: 63 µmol 3.1, 69.3 umol NBS and 0.79-6.3 µmol acid additive in AcOH (0.25M) irradiated with 470 nm LEDs at 60 mW (level 1) intensity for 65 min. 205 Figure B.1. 3. Plateau detection test with algorithm 1. Testing data selected from the third and fourth rounds of automated high-throughput reaction profiling. 206 Figure B.1. 4. Plateau detection test with algorithm 2. Testing data selected from the third and fourth rounds of automated high-throughput reaction profiling. 207 Figure B.1. 5. Plateau detection test with algorithm 3. Testing data selected from the third and fourth rounds of automated high-throughput reaction profiling. 208 Figure B.1. 6. Gaussian process (GP) model for three consecutive optimization iterations in campaign 3. Also displayed are the next suggested sampling points through three acquisition functions, including expected improvement (EI), probability of improvement (PI) and upper confidence bound (UCB). Red line: simulated objective function from all the combined optimization data modeled through neural networks; green line with green shading: GP model with 95% confidence interval; red point: previous sampling point; blue, brown and purple points: next suggested sampling point by the acquisition function. 209 Figure B.1. 7. Simulated optimization runs based on a neural network model of the combined data. Each simulated optimization was repeated 100 times to achieve a means of statistically significant comparison between the three optimization strategies. 210 B.2 Spectral data for compounds 3.1, 3.2 and 3.3. Figure B.3. 1. MS ESI+ of 3.1. Figure B.3. 2. MS ESI+ of 3.2. Figure B.3. 3. MS ESI+ of 3.3. 211 Figure B.3. 4. 1H and 13C NMR spectra of 3.1 in DMSO-d6. 212 Figure B.3. 5. 1H and 13C NMR spectra of 3.2 in DMSO-d6. 213 Figure B.3. 6. 1H and 13C NMR spectra of 3.3 in DMSO-d6. 214 B.3 Analytical methods Reaction analyses were carried out using an Agilent 1100 HPLC-UV using the method below: Table B.4. 1. HPLC-UV method for purity determination Column: Agilent Poroshell 210 PFP 2.7 um, 4.6 x 50 mm Column Temperature: 50 oC Flow Rate: 0.85 ml/min Detection: 210 nm Acquisition Time: 3.0 min Mobile Phase: Solvent A = 2 mM ammonium formate in water; Solvent B = 2 mM ammonium formate in acetonitrile 10% water Mobile Phase Program: Time 0.0 min 1.5 min 2.5 min 3.0 min B% 5 100 100 5 Injection Volume: 2 µL 215 Compound Name: Retention time: 6-methyl-4-(trifluoromethyl)pyridazin-3(2H)-one 3.1 0.99 min 6-(bromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.2 1.27 min 6-(dibromomethyl)-4-(trifluoromethyl)pyridazin-3(2H)-one 3.3 1.48 min Example Chromatogram 216 B.4 Chemspeed protocol Below is an excerpt of the Chemspeed protocol used in autonomous optimization. 217 Figure B.5. 1. Chemspeed protocol for autonomous optimization of the Wohl-Ziegler reaction
Thesis/Dissertation
eng
Chemistry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hein, Jason
The development and applications of autonomous process optimization systems
Text