Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks

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.

Automated summary

The study demonstrates that convolutional recurrent neural networks can learn underlying physical rules and replace PDE-based simulations to predict microstructure phenomena. Trained networks accurately predict both short-term local dynamics and long-term statistical properties of microstructures, showing advantages in time-stepping efficiency and offering an alternative when material parameters or governing PDEs are not well determined.