Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolation

TL;DR

This paper introduces a deep learning framework using CVAE and interpolation techniques to efficiently predict microstructure evolution in materials, significantly reducing computational costs compared to traditional phase-field models.

Contribution

The work presents a novel deep learning approach combining CVAE, cubic spline interpolation, and SLERP for fast, accurate microstructure prediction across compositions.

Findings

High visual and statistical similarity to phase-field simulations

Effective prediction of microstructure evolution for intermediate compositions

Accelerated surrogate modeling for materials design

Abstract

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.