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Atomistic simulations of kinetic processes in complex atomic environments Soltanibajestani, Siavash


Microstructure evolution of materials is derived by kinetic processes that are atomistic in nature. Phenomena like grain boundary migration, the formation and growth of crystalline phases in bulk metallic glasses and structural relaxation in amorphous materials are examples of microstructural phenomena that are derived from atomic scale dynamics. Probing such processes in disordered atomic environments is challenging experimentally since they operate at small length scales (nanometers) and time scales (nanoseconds). In this work, we employ molecular dynamics simulations and a variety of dynamical coarse-graining methods to bridge the gap between microscopic processes and macroscopic observables. First, the diffusion kinetics of carbon in Fe-C glasses is studied. By detecting individual atomic hops, we quantify the parameters that control the diffusivity, namely jump length, residence time and correlation factor. Our results help explain the experimentally observed increase in stability of metal-metalloid glasses against crystallization with increasing carbon concentration. Next, the dynamical processes and structural relaxations in a model glassy system are explored using a machine learning algorithm involving neural networks combined with Markov State Models with the aim of identifying previously unexplored dynamical processes that may be crucial for understanding the complex behaviour of metallic glasses. Finally, the kinetic processes governing grain boundary (GB) motion are studied using the same approach as was used for the glasses. The GB mobility is extracted from three GBs in iron using both conventional techniques as well as Markov State Models. The Markov State Model is shown to also provide insights into the intra-GB processes that govern the temperature dependence of GB motion.

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