Uncovering atomistic mechanisms of crystallization using Machine Learning

Abstract:

Solid-liquid interfaces have notoriously haphazard atomic environments. While essentially amorphous, the liquid has short-range order and heterogeneous dynamics. The crystal, albeit ordered, contains a plethora of defects ranging from adatoms to dislocation-created spiral steps. All these elements are of paramount importance in the crystal growth process, which makes the crystallization kinetics challenging to describe concisely in a single framework. In this seminar I will introduce a novel data-driven approach to systematically detect, encode, and classify all atomic-scale mechanisms of crystallization. I will also show how this approach naturally leads to a predictive kinetic model of crystallization that takes into account the entire zoo of microstructural elements present at solid-liquid interfaces. In this innovative application of data science to materials, Machine Learning is employed to augment rather than substitute human intuition. The result is an approach that blends prevailing scientific methods with data-science tools to produce physically-consistent models and novel conceptual knowledge.

Bio:

Rodrigo Freitas is a postdoctoral researcher in the Department of Materials Science at Stanford University. He received B.Sc. and M.Sc. degrees in Physics from the University of Campinas (Brazil), and M.Sc. and Ph.D. degrees in Materials Science and Engineering from the University of California Berkeley. During his Ph.D. he was also a Livermore Graduate Scholar in the Materials Science Division of the Lawrence Livermore National Laboratory. Rodrigo’s research is focused on elucidating the fundamental mechanisms of microstructural evolution for systems of relevance in materials science broadly construed. His work aims to bridge the gap between the all-atom information gathered from atomistic simulations and the mesoscale description of microstructural elements employed in materials science.