Material Microstructure Design Using VAE-Regression with Multimodal
  Prior
Avadhut Sardeshmukh Sreedhar Reddy BP Gautham Pushpak Bhattacharyya
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.