  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, the description of compounds as
vectors of components' proportions is characterized by discrete features and a
severe sparsity. Furthermore, traditional generative model validation processes
as visual verification, FID and Inception scores are tailored for images and
cannot then be used as such in this context. To tackle these issues, we develop
an original Binded-VAE model dedicated to the generation of discrete 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.
