论文标题
通过深度学习预测无定形固体中静态结构的塑性敏感性
Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
论文作者
论文摘要
建立无定形固体中的结构特性关系一直是一项长期的材料科学挑战。在这里，我们介绍了旋转变化的局部结构表示，该表示可以为不同的负载方向做出不同的预测，这对于高保真预测压力驱动的剪切转换的倾向至关重要。这种新颖的结构表示与卷积神经网络（CNN）结合使用，这是一种强大的深度学习算法，会导致前所未有的准确性，用于鉴定具有较高剪切转化倾向（即塑性易感性）的原子，仅来自静态结构 - 空间原子位置 - 在两种和三层模型中 - 在两层和三层模型中。在一个组合物上对样品进行训练的数据驱动模型和给定的加工历史记录可以转移到具有不同加工历史的玻璃样品或同一合金系统中不同组成的玻璃样品。我们对新结构表示形式的分析还为关键的原子包装特征提供了宝贵的见解，这些特征会影响局部机械响应及其在眼镜中的各向异性。
It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotation-variant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural network (CNN), a powerful deep learning algorithm, leads to unprecedented accuracy for identifying atoms with high propensity for shear transformations (i.e., plastic susceptibility), solely from the static structure - the spatial atomic positions - in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses.
