Mińkowski Marcin, Laurson Lasse
Computational Physics Laboratory, Tampere University, P.O. Box 692, FI-33014, Tampere, Finland.
Sci Rep. 2023 Aug 26;13(1):13977. doi: 10.1038/s41598-023-40974-0.
Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study the predictability of the sample-dependent shear moduli and yield stresses of a large set of small cube-shaped iron polycrystals generated by Voronoi tessellation, by combining molecular dynamics simulations and machine learning. Training a convolutional neural network to infer the mapping between the initial polycrystalline structure of the samples and features of the ensuing stress-strain curves reveals that the shear modulus can be predicted better than the yield stress. We discuss our results in the context of the sensitivity of the system's response to small perturbations of its initial state.
晶体材料的变形是复杂系统行为的一个有趣例子。小样本通常对外加应力表现出类似随机的不规则响应，表现为其力学性能在样本间存在显著差异。在这项工作中，我们通过结合分子动力学模拟和机器学习，研究了通过Voronoi镶嵌生成的大量小立方体形铁多晶体的与样本相关的剪切模量和屈服应力的可预测性。训练一个卷积神经网络来推断样本的初始多晶结构与随后应力-应变曲线特征之间的映射，结果表明，剪切模量比屈服应力能得到更好的预测。我们在系统响应对于其初始状态小扰动的敏感性背景下讨论了我们的结果。
