Date of this Version
Physical Review Letters
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.
© 2015 American Physical Society. You can view the original article at: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.108001
Cubuk, E. D., Schoenholz, S., Rieser, J. M., Malone, B. D., Rottler, J., Durian, D. J., Kaxiras, E., & Liu, A. J. (2015). Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods. Physical Review Letters, 114 (10), 108001-1-108001-5. http://dx.doi.org/10.1103/PhysRevLett.114.108001
Date Posted: 13 October 2017
This document has been peer reviewed.