Structure-property relationships from universal signatures of plasticity in disordered solids.
Structure-property relationships from universal signatures of plasticity in disordered solids.
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DOI：
10.1126/science.aai8830
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发表时间：
2017-11-24
期刊：
影响因子：
--
通讯作者：
Liu AJ
中科院分区：
文献类型：
--
作者：
Cubuk ED;Ivancic RJS;Schoenholz SS;Strickland DJ;Basu A;Davidson ZS;Fontaine J;Hor JL;Huang YR;Jiang Y;Keim NC;Koshigan KD;Lefever JA;Liu T;Ma XG;Magagnosc DJ;Morrow E;Ortiz CP;Rieser JM;Shavit A;Still T;Xu Y;Zhang Y;Nordstrom KN;Arratia PE;Carpick RW;Durian DJ;Fakhraai Z;Jerolmack DJ;Lee D;Li J;Riggleman R;Turner KT;Yodh AG;Gianola DS;Liu AJ
当变形超过其弹性极限时，结晶固体通过结构缺陷周围的颗粒重排而塑性流动。无序的固体也流动，但没有明显的结构缺陷。我们通过微观结构量“柔软度”将无序固体的结构与塑性联系起来，“柔软度”是由机器学习设计的，可以最大限度地预测重排。实验结果和计算使我们能够测量的空间相关性和应变响应的柔软性，以及两个措施的可塑性：重排的大小和屈服应变。这四个量在从原子到颗粒的无序堆积的物体的值中保持了显着的共同性，直径跨越7个数量级，弹性模量跨越13个数量级。这些共性链接的空间相关性和应变响应的软重排尺寸和屈服应变，分别。
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.
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影响因子：
8.6
作者：
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