Department of Physics Papers

Document Type

Journal Article

Date of this Version

3-9-2015

Publication Source

Physical Review Letters

Volume

114

Issue

10

Start Page

108001-1

Last Page

108001-5

DOI

10.1103/PhysRevLett.114.108001

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.

Copyright/Permission Statement

© 2015 American Physical Society. You can view the original article at: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.108001

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Date Posted:10 October 2017

This document has been peer reviewed.