Phase-field modeling has been a useful method for describing crystal defects (such as dislocations and grain boundaries) at the continuous level. However, energy functional based on the crystallography and elasticity, as well as the kinetic governing equations for seeking the equilibrium state, may prevent it from widespread application. Therefore, surrogate models that directly map material properties (i.e., the γ-surfaces) to the equilibrium dislocation configurations are highly necessary from the view of improving both practical usability and computing efficiency. Using output data from phase-field calculation, the current work trains a simple neural network (NN) to investigate the geometrical outline of GB dislocations. Moreover, a deep NN concerning complex input and output is constructed to predict the whole configuration of the dislocation networks and stacking faults. The two NNs build up the relationship between the γ-surface and the GB dislocation configurations, which are validated through phase-field simulation in this work and atomic simulation reported previously. This work provides an important framework that bridges the physics-based model and machine learning techniques through data generation and transmission.

中文翻译：

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通过多维神经网络和高通量相场计算预测晶界位错结构


相场建模是连续水平描述晶体缺陷（例如位错和晶界）的有用方法。然而，基于晶体学和弹性的能量泛函以及寻求平衡状态的动力学控制方程可能会阻碍其广泛应用。因此，从提高实际可用性和计算效率的角度来看，直接将材料属性（即γ表面）映射到平衡位错配置的替代模型是非常必要的。当前的工作使用相场计算的输出数据训练一个简单的神经网络（NN）来研究晶界位错的几何轮廓。此外，构建了一个涉及复杂输入和输出的深度神经网络来预测位错网络和堆垛层错的整体配置。这两个神经网络建立了 γ 表面和 GB 位错配置之间的关系，这些关系通过本工作中的相场模拟和之前报道的原子模拟进行了验证。这项工作提供了一个重要的框架，通过数据生成和传输将基于物理的模型和机器学习技术联系起来。