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

Abstract

Summary 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.

Predicting material microstructure evolution via data-driven machine learning

“…Several key findings from Yang et al.’s 9 work include that the RNN architecture developed can (1) generalize well beyond training datasets over long time periods up to 10 times the training data’s time span, (2) be applied to larger images than the training set with comparable accuracy, (3) predict evolution of microstructures with different morphologies than the training dataset, and (4) employ time steps 1–2 orders of magnitude larger than PDE-based simulations.…”

“…In the May 14, 2021 issue of Patterns , Yang et al. 9 offer a new alternative to partial differential equation (PDE)-based simulations using a data-driven approach employing recurrent neural networks (RNN) in their article “self-supervised learning and prediction of microstructure evolution with recurrent neural networks.”…”

“…This study demonstrates that a well-trained RNN can not only serve as a PDE emulator but also infer implicit material properties from spatiotemporal data and provides a representation of the targeted problems that lowers data demand and improves training and prediction efficiency. The architecture and approach detailed in Yang et al.’s 9 study has wide applicability. In particular, this approach may be applied to the analysis of both two- and three-dimensional microscopy data collected during in situ or in operando experimentation to further improve our understanding of the spatiotemporal evolution of material microstructures.…”

“…This work represents a timely, important advancement in the development of reliable computational methodologies using neural networks that can provide advantages over traditional approaches for predictive materials modeling. Hence, Yang et al.’s 9 approach and findings have implications to a wide range of applications in the materials research community.…”

Comparing transfer learning to feature optimization in microstructure classification

“…An alternative to physics-based models is statistical, data-driven models that trade accuracy for speed by leveraging correlations and patterns in data. Machine learning models have been used extensively to analyze various microstructural morphologies over the last two decades ( Yang et al., 2021 ). One such example is the development of optimal morphology derivation from a given microstructure using Bayesian optimization and kinetic Monte Carlo simulation ( Tran et al., 2020 ).…”