Yang Kaiqi, Cao Yifan, Zhang Youtian, Fan Shaoxun, Tang Ming, Aberg Daniel, Sadigh Babak, Zhou Fei
Department of Materials Science and NanoEngineering, Rice University, Houston, TX 77005, USA.
Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
Patterns (N Y). 2021 Apr 22;2(5):100243. doi: 10.1016/j.patter.2021.100243. eCollection 2021 May 14.
Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena. Neural nets are trained by self-supervised learning with image sequences from simulations of several common processes, including plane-wave propagation, grain growth, spinodal decomposition, and dendritic crystal growth. The trained networks can accurately predict both short-term local dynamics and long-term statistical properties of microstructures assessed herein and are capable of extrapolating beyond the training datasets in spatiotemporal domains and configurational and parametric spaces. Such a data-driven approach offers significant advantages over PDE-based simulations in time-stepping efficiency and offers a useful alternative, especially when the material parameters or governing PDEs are not well determined.
微观结构演变是理解和利用材料加工-结构-性能关系的关键方面。微观结构演变建模通常依赖于用偏微分方程（PDEs）描述演变原理的粗粒度模拟。在此，我们证明卷积循环神经网络可以学习潜在的物理规则，并在微观结构现象预测中取代基于PDE的模拟。通过对包括平面波传播、晶粒生长、旋节线分解和树枝状晶体生长在内的几种常见过程模拟的图像序列进行自监督学习来训练神经网络。训练后的网络能够准确预测本文评估的微观结构的短期局部动力学和长期统计特性，并且能够在时空域以及构型和参数空间中超越训练数据集进行外推。这种数据驱动的方法在时间步长效率方面比基于PDE的模拟具有显著优势，并且提供了一种有用的替代方法，特别是当材料参数或控制PDEs不确定时。
