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

被引：336

作者：

Cubuk, E. D. ^{[1
,2
]}

Schoenholz, S. S. ^{[3
]}

Rieser, J. M. ^{[3
]}

Malone, B. D. ^{[1
,2
]}

Rottler, J. ^{[4
]}

Durian, D. J. ^{[3
]}

Kaxiras, E. ^{[1
,2
]}

Liu, A. J. ^{[3
]}

机构：

[1] Harvard Univ, Dept Phys, Cambridge, MA 02138 USA

[2] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA

[3] Univ Penn, Dept Phys, Philadelphia, PA 19104 USA

[4] Univ British Columbia, Dept Phys & Astron, Vancouver, BC V6T 1Z4, Canada

来源：

PHYSICAL REVIEW LETTERS | 2015年 / 114卷 / 10期

基金：

美国国家科学基金会;

关键词：

SUPERCOOLED LIQUID; DEFORMATION; DYNAMICS;

D O I：

10.1103/PhysRevLett.114.108001

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页数：5

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