Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolation
This provides an efficient surrogate model for materials scientists to optimize alloy compositions, though it is incremental as it builds on existing deep learning and interpolation techniques.
This work tackles the computational expense of phase-field models for simulating microstructure evolution by developing a deep learning framework using a Conditional Variational Autoencoder with latent space interpolation, achieving high visual and statistical similarity to simulations and enabling accelerated materials design.
Phase-field models accurately simulate microstructure evolution, but their dependence on solving complex differential equations makes them computationally expensive. This work achieves a significant acceleration via a novel deep learning-based framework, utilizing a Conditional Variational Autoencoder (CVAE) coupled with Cubic Spline Interpolation and Spherical Linear Interpolation (SLERP). We demonstrate the method for binary spinodal decomposition by predicting microstructure evolution for intermediate alloy compositions from a limited set of training compositions. First, using microstructures from phase-field simulations of binary spinodal decomposition, we train the CVAE, which learns compact latent representations that encode essential morphological features. Next, we use cubic spline interpolation in the latent space to predict microstructures for any unknown composition. Finally, SLERP ensures smooth morphological evolution with time that closely resembles coarsening. The predicted microstructures exhibit high visual and statistical similarity to phase-field simulations. This framework offers a scalable and efficient surrogate model for microstructure evolution, enabling accelerated materials design and composition optimization.