Cubuk E D, Ivancic R J S, Schoenholz S S, Strickland D J, Basu A, Davidson Z S, Fontaine J, Hor J L, Huang Y-R, Jiang Y, Keim N C, Koshigan K D, Lefever J A, Liu T, Ma X-G, Magagnosc D J, Morrow E, Ortiz C P, Rieser J M, Shavit A, Still T, Xu Y, Zhang Y, Nordstrom K N, Arratia P E, Carpick R W, Durian D J, Fakhraai Z, Jerolmack D J, Lee Daeyeon, Li Ju, Riggleman R, Turner K T, Yodh A G, Gianola D S, Liu Andrea J
Google Brain, Mountain View, CA 94043, USA.
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA.
Science. 2017 Nov 24;358(6366):1033-1037. doi: 10.1126/science.aai8830.
When deformed beyond their elastic limits, crystalline solids flow plastically via particle rearrangements localized around structural defects. Disordered solids also flow, but without obvious structural defects. We link structure to plasticity in disordered solids via a microscopic structural quantity, "softness," designed by machine learning to be maximally predictive of rearrangements. Experimental results and computations enabled us to measure the spatial correlations and strain response of softness, as well as two measures of plasticity: the size of rearrangements and the yield strain. All four quantities maintained remarkable commonality in their values for disordered packings of objects ranging from atoms to grains, spanning seven orders of magnitude in diameter and 13 orders of magnitude in elastic modulus. These commonalities link the spatial correlations and strain response of softness to rearrangement size and yield strain, respectively.
当晶体固体变形超过其弹性极限时，会通过围绕结构缺陷局部化的粒子重排进行塑性流动。无序固体也会流动，但没有明显的结构缺陷。我们通过一种微观结构量“柔软度”将无序固体中的结构与可塑性联系起来，该量由机器学习设计，能够最大程度地预测重排。实验结果和计算使我们能够测量柔软度的空间相关性和应变响应，以及两种可塑性度量：重排大小和屈服应变。对于从原子到颗粒的物体无序堆积，所有这四个量在其值上都保持了显著的共性，这些物体的直径跨越七个数量级，弹性模量跨越十三个数量级。这些共性分别将柔软度的空间相关性和应变响应与重排大小和屈服应变联系起来。
