Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

• Published in: npj computational materials Vol. 7; no. 1; pp. 1 - 11
• Main Authors: Montes de Oca Zapiain, David, Stewart, James A., Dingreville, Rémi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.01.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1023; 639/301/1034; 639/301/119/1002; Accuracy; Adaptive algorithms; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Computer applications; Computer simulation; Equations of motion; Evolution; Learning algorithms; Long short-term memory; Machine learning; MATERIALS SCIENCE; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Microstructure; Neural networks; Numerical integration; Performance prediction; Simulation; Spinodal decomposition; structural materials; structure of solids and liquids; Theoretical; theory and computation
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-020-00471-8

The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.