Material Microstructure Design Using VAE-Regression with Multimodal Prior

TL;DR

This paper introduces a VAE-regression model with a multimodal prior for efficient forward and inverse microstructure-property prediction in materials science, enabling accurate property prediction and diverse microstructure generation.

Contribution

The paper presents a novel VAE-based framework with a multimodal prior that jointly learns structure-property linkages for both forward and inverse predictions in materials science.

Findings

Achieves state-of-the-art accuracy in forward property prediction.

Enables direct inverse microstructure inference without optimization loops.

Infers multiple microstructures for target properties using a multimodal prior.

Abstract

We propose a variational autoencoder (VAE)-based model for building forward and inverse structure-property linkages, a problem of paramount importance in computational materials science. Our model systematically combines VAE with regression, linking the two models through a two-level prior conditioned on the regression variables. The regression loss is optimized jointly with the reconstruction loss of the variational autoencoder, learning microstructure features relevant for property prediction and reconstruction. The resultant model can be used for both forward and inverse prediction i.e., for predicting the properties of a given microstructure as well as for predicting the microstructure required to obtain given properties. Since the inverse problem is ill-posed (one-to-many), we derive the objective function using a multi-modal Gaussian mixture prior enabling the model to infer multiple microstructures for a target set of properties. We show that for forward prediction, our model is as accurate as state-of-the-art forward-only models. Additionally, our method enables direct inverse inference. We show that the microstructures inferred using our model achieve desired properties reasonably accurately, avoiding the need for expensive optimization loops.