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

TitleIdentifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
Publication TypeJournal Article
Year of Publication2015
AuthorsCubuk, E.  D., S.  S. Schoenholz, J.  M. Rieser, B.  D. Malone, J. Rottler, D.  J. Durian, E. Kaxiras, and A.  J. Liu
JournalPhysical Review Letters
Volume114
Pagination108001
AbstractWe 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.
URLhttp://link.aps.org/doi/10.1103/PhysRevLett.114.108001
DOI10.1103/PhysRevLett.114.108001
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