A Binded VAE for Inorganic Material Generation
This work addresses the expensive and time-consuming process of industrial material design, though it appears incremental as it adapts existing VAE methods to a specific domain.
The authors tackled the problem of generating realistic inorganic materials with discrete and sparse features by developing a Binded-VAE model, showing it outperforms standard generative models in rubber compound design.
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.