Accelerating microstructure modeling via machine learning: A method combining Autoencoder and ConvLSTM

Abstract

Phase-field modeling is an elegant and versatile computation tool to predict microstructure evolution in materials in the mesoscale regime. However, these simulations require rigorous numerical solutions of differential equations, which are accurate but computationally expensive. To overcome this difficulty, we combine two popular machine-learning techniques, autoencoder and convolutional long short-term memory (ConvLSTM), to accelerate the study of microstructural evolution without compromising the resolution of the microstructural representation. After training with phase-field-generated microstructures of 10 known compositions, the model can accurately predict the microstructure for the future $n\mathrm{th}$ frames based on the previous $m$ frames for an unknown composition. Replacing $n$ phase-field steps with machine-learned microstructures can significantly accelerate the in silico study of microstructure evolution.