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Hendy, Mohamed

University of British Columbia

This thesis presents the development of advanced computational frameworks tailored for rational materials design, with a particular focus on high-entropy alloys (HEAs) and nano-crystalline materials for energy applications. The research is divided into three main components, each addressing critical aspects of material behavior for energy-related applications. The first component introduces a temporal multi-scale multi-physics framework for simulating the long-term diffusion of radiation-induced defects in nano-crystalline materials. By integrating molecular dynamics (MD) simulations with electronic effects and atomistic diffusion models, this framework elucidates the self-healing mechanisms of nano-crystalline materials under radiation, revealing how grain boundaries effectively absorb interstitial atoms and migrate vacancies, thereby reducing defect populations over time. The second component focuses on the inclusion of electronic effects in atomistic simulations of HEAs exposed to radiation. A spatial multi-scale framework, featuring modifications to the ℓ2T-MD method, is developed to accurately model the impact of electronic properties on defect formation. The third component tackles the exploration of HEA configurational space for catalytic applications. A machine learning framework, aiming to correct the estimations of the alchemical perturbation density functional theory (APDFT), is developed to predict binding energies on HEA surfaces with high accuracy and minimal computational cost. This approach enables efficient high-throughput screening of HEAs for catalytic processes, such as carbon dioxide reduction, thereby facilitating the discovery of optimal catalytic materials. Collectively, this thesis contributes to advancement of the field of computational materials science by introducing novel methodologies that enhance predictive capabilities for HEAs and nano-crystalline materials. These contributions are pivotal in optimizing material properties for radiation resistance and catalytic performance, offering significant potential for advancing sustainable energy technologies and addressing pressing environmental challenges.

Text

2024-12-16

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89884

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/