Identifying structural flow defects in disordered solids using machine learning methods

TL;DR

This paper demonstrates that machine learning applied to local structural features can effectively identify flow defects in various disordered solids, revealing subtle structural indicators of regions prone to rearrangement.

Contribution

The study introduces a machine learning approach to detect flow defects in disordered materials across different systems and dimensions, highlighting structural features linked to dynamic heterogeneity.

Findings

Successful identification of flow defects in granular and glassy systems

Structural features differentiate flow defects from other regions

Method applicable across multiple dimensions and temperature regimes

Abstract

We use machine learning methods on local structure to identify flow defects - or regions susceptible to rearrangement - in jammed and glassy systems. We apply this method successfully to two disparate systems: a two dimensional experimental realization of a granular pillar under compression, and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.