Machine Learning Surrogate Model for Acceleration of Ferroelectric Phase-Field Modeling

Phase-field modeling is a powerful technique for predicting domain structure evolution and electromechanical properties of ferroelectric materials. However, it remains computationally very expensive, thus demanding high computing resources and restraining its use for exploring large systems. Some machine learning approaches have already been proposed to accelerate general phase-field simulations. Here, we present a specifically neural-network-trained model for ferroelectric phase-field modeling, including supervised and nonsupervised learning from Landau energy. A surrogate model predicts the microstructural polarization field evolution determining the electrostatic and mechanical equilibrium at each time step. The model produces accurate and stable rollout predictions, up to hundreds of frames even when starting from the beginning of the simulation. With a relative error contained below 5% compared to a high-fidelity phase field, we show that our model can be used instead of real solvers to conduct full simulations. While being at least 685 times faster than the classical phase-field computation, our approach opens a path to explore ferroelectric materials at a larger scale with fewer computing resources.