Investigating Atomic-Scale Mechanisms of Crystallization Using Machine Learning

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 variety 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 talk I will present data-driven approaches to systematically detect, encode, and classify atomic-scale mechanisms of crystallization. It will also be shown 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. The result is an approach that blends prevailing scientific methods with data-science tools to produce physically-consistent models and novel conceptual knowledge