Keywords: VAE, Generative Design, Inorganic Material Generation, Deep Generative Models
TL;DR: We propose a Binded-VAE for inorganic material generation and novel metrics adapted to the problem.
Abstract: Designing new industrial materials with desired properties can be very expensive and time consuming. The main difficulty is to generate compounds that correspond to realistic materials. Indeed, description of the compounds as vectors of components' proportions is characterized by a severe sparsity. Furthermore, traditional generative model validation processes as visual verification, FID and Inception scores cannot be used in this context. To tackle these issues, we develop an original Binded-VAE model tailored to generate sharp datasets with high sparsity. We validate the model with novel metrics adapted to the problem of compounds generation. We show on a real issue of rubber compound design that the proposed approach outperforms the standard generative models which opens new perspectives for material design optimization.