Identifying {Structural} {Flow} {Defects} in {Disordered} {Solids} {Using} {Machine}-{Learning} {Methods}

Publication Type
Journal Article
Year of Publication
2015
Authors
Cubuk, E. D.
Schoenholz, S. S.
Rieser, J. M.
Malone, B. D.
Rottler,J.
Durian, D. J.
Kaxiras, E.
Liu, A. J.
Name of Publication
Physical Review Letters
Volume
114
Pagination
108001
Abstract

We use machine-learning methods on local structure to identify flow defects—or particles susceptible to rearrangement—in jammed and glassy systems. We apply this method successfully to two very different 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.