Yang, KaiqiCao, YifanZhang, YoutianFan, ShaoxunTang, MingAberg, DanielSadigh, BabakZhou, Fei2021-06-072021-06-072021Yang, Kaiqi, Cao, Yifan, Zhang, Youtian, et al.. "Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks." <i>Patterns,</i> 2, no. 5 (2021) Elsevier: https://doi.org/10.1016/j.patter.2021.100243.https://hdl.handle.net/1911/110667Microstructural 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.engThis is an open access article under the CC BY-NC-ND licenseJournal article1-s2-0-S2666389921000635-mainhttps://doi.org/10.1016/j.patter.2021.100243