Material Microstructure Design Using VAE-Regression with Multimodal Prior

arXiv:2402.17806v1 Announce Type: new
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 …

abstract arxiv autoencoder building computational cond-mat.mtrl-sci cs.lg design importance loss material materials materials science multimodal prior property regression science stat.ml through type vae variables