A Binded VAE for Inorganic Material Generation
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.
Code (1)
Similar Papers 제목 키워드 기반
A Padding Method for Enhanced Encoding of Inorganic Structures with Varying Chemical Compositions
Designing novel inorganic materials through generative models remains an important challenge for material science, driven by the complexity and diversity of inorganic structures across expansive chemical compositions and…
Computational EfficiencyAutonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
Conventional machine learning approaches accelerate inorganic materials design via accurate property prediction and targeted material generation, yet they operate as single-shot models limited by the latent knowledge bak…
Study of Deep Generative Models for Inorganic Chemical Compositions
Generative models based on generative adversarial networks (GANs) and variational autoencoders (VAEs) have been widely studied in the fields of image generation, speech generation, and drug discovery, but, only a few stu…
Drug DiscoveryImage GenerationGenerative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit b…
Generative Adversarial NetworkvalidDeep Reinforcement Learning for Inverse Inorganic Materials Design
A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and synthesis of those materials. In this wor…
Deep Reinforcement LearningDiversityFormation Energyreinforcement-learning+2