  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 ten known compositions, the model can accurately predict the
microstructure for the future nth frames based on 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.
