The dynamics of machine-learned "softness" in supercooled liquids describe dynamical heterogeneity

TL;DR

This paper develops a model linking machine-learned softness to the structural origins of dynamical heterogeneity in supercooled liquids, capturing facilitation and long-range correlations.

Contribution

It introduces a model that uses softness to explain dynamical heterogeneity and facilitation, respecting time-reversal symmetry, with implications for understanding long-range correlations.

Findings

Model reproduces key features of dynamical heterogeneity

Softness influences particle rearrangement probabilities

Long-range correlations emerge from short-range softness interactions

Abstract

The dynamics of supercooled liquids slow down and become increasingly heterogeneous as they are cooled. Recently, local structural variables identified using machine learning, such as "softness", have emerged as predictors of local dynamics. Here we construct a model using softness to describe the structural origins of dynamical heterogeneity in supercooled liquids. In our model, the probability of particles to rearrange is determined by their softness, and each rearrangement induces changes in the softness of nearby particles, describing facilitation. We show how to ensure that these changes respect the underlying time-reversal symmetry of the liquid's dynamics. The model reproduces the salient features of dynamical heterogeneity, and demonstrates how long-ranged dynamical correlations can emerge at long time scales from a relatively short softness correlation length.