论文标题
机器学习晶体的塑性变形
Machine learning plastic deformation of crystals
论文作者
论文摘要
微米尺度晶体固体的塑性变形表现出具有显着样品与样本变化的应力 - 应变曲线。如果这种变异性纯粹是随机的，或者在某种程度上可预测的是，这是一个相关的问题。在这里，我们通过采用机器学习技术（例如回归神经网络和支持矢量机）来显示，将变形可预测性随应变和晶体尺寸演变而来。使用来自离散脱位动力学模拟的数据，对机器学习模型进行了训练，以将映射从现有的错位配置的特征推断为应力 - 应变曲线。可预测性与应变关系是非单调的，并且表现出系统尺寸效应：较大的系统更可预测。随机变形雪崩产生了中间菌株变形可预测性的基本限制。但是，可以很好地预测样品的大型变形动力学。
Plastic deformation of micron-scale crystalline solids exhibits stress-strain curves with significant sample-to-sample variations. It is a pertinent question if this variability is purely random or to some extent predictable. Here we show, by employing machine learning techniques such as regression neural networks and support vector machines that deformation predictability evolves with strain and crystal size. Using data from discrete dislocations dynamics simulations, the machine learning models are trained to infer the mapping from features of the pre-existing dislocation configuration to the stress-strain curves. The predictability vs strain relation is non-monotonic and exhibits a system size effect: larger systems are more predictable. Stochastic deformation avalanches give rise to fundamental limits of deformation predictability for intermediate strains. However, the large-strain deformation dynamics of the samples can be predicted surprisingly well.
