Deciphering a structural signature of glass dynamics by machine learning

Abstract

The dynamics of atoms plays a key role in governing various dynamical and transport properties of glasses. However, it remains elusive which structural features (if any) control atom dynamics in glasses. Here, based on million-atom molecular dynamics simulations and classification-based machine learning, we extract a needle in a haystack by identifying a local, nonintuitive structural signature (a revised version of the recently developed softness metric) that governs glass dynamics. We do so by investigating the ion mobility in sodium silicate glasses---a realistic, archetypal glass---finding that the sodium ion mobility is largely encoded in its initial softness, wherein softer Na atoms exhibit higher mobility. Importantly, our approach allows us to interpret the machine-learned softness metric and thus elucidate the atomistic origin of the ion mobility. Namely, we find that Na mobility is anticorrelated with the local density of defect oxygen neighbors that are located between the nearest two coordination shells. This local packing order offers a potential path to develop glass formulations with tailored dynamical properties. Finally, we demonstrate that the softness is strongly anticorrelated with the activation energy for Na atom reorganization.