Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
View/ Open
Author
Cubuk, E. D.
Schoenholz, S. S.
Rieser, J. M.
Malone, B. D.
Rottler, J.
Durian, D. J.
Kaxiras, E.
Liu, A. J.
Published Version
https://doi.org/10.1103/PhysRevLett.114.108001Metadata
Show full item recordCitation
Cubuk, E. D., S. S. Schoenholz, J. M. Rieser, B. D. Malone, J. Rottler, D. J. Durian, E. Kaxiras, and A. J. Liu. 2015. “Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods.” Physical Review Letters 114 (10). https://doi.org/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.Terms of Use
This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAPCitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:41384122
Collections
- FAS Scholarly Articles [18172]
Contact administrator regarding this item (to report mistakes or request changes)