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.
@article{arxiv.1409.6820,
title = {Identifying structural flow defects in disordered solids using machine learning methods},
author = {Ekin D. Cubuk and Samuel S. Schoenholz and Jennifer M. Rieser and Brad D. Malone and Joerg Rottler and Douglas J. Durian and Efthimios Kaxiras and Andrea J. Liu},
journal= {arXiv preprint arXiv:1409.6820},
year = {2015}
}