Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
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DOI：
10.1103/physrevlett.114.108001
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发表时间：
2015-03-09
影响因子：
8.6
通讯作者：
Liu, A. J.
中科院分区：
文献类型：
--
作者：
Cubuk, E. D.;Schoenholz, S. S.;Liu, A. J.
我们在局部结构上使用机器学习方法来识别堵塞和玻璃系统中的流动缺陷或易受阻碍的颗粒。我们成功地将这种方法应用于两个非常不同的系统：一个二维的实验实现的颗粒柱压缩和Lennard-Jones玻璃在两个和三个维度以上和以下的玻璃化转变温度。我们还确定了流动缺陷的特征，将其与样品的其余部分区分开来。我们的研究结果表明，它是可能的，以辨别微妙的结构特征，负责在广泛的无序材料中观察到的异质动力学。
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.