Material Microstructure Design Using VAE-Regression with Multimodal Prior
This addresses a key challenge in materials science by providing a unified model for forward and inverse predictions, though it is incremental as it builds on existing VAE and regression methods.
The paper tackles the problem of predicting material properties from microstructures and vice versa in computational materials science, proposing a VAE-regression model that achieves accuracy comparable to state-of-the-art forward-only models and enables direct inverse inference with reasonable accuracy, avoiding costly optimization.
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.