Study of Deep Generative Models for Inorganic Chemical Compositions

2019/10/25 by Yoshihide Sawada, Sawada, Yoshihide, Koji Morikawa +3 · 1 citation
Computer Science · Materials Science · #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1910.11499

openalex publication_date 2019/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 studies have focused on the generation of inorganic materials. Such studies use the crystal structures of materials, but material researchers rarely store this information. Thus, we generate chemical compositions without using crystal information. We use a conditional VAE (CondVAE) and a conditional GAN (CondGAN) and show that CondGAN using the bag-of-atom representation with physical descriptors generates better compositions than other generative models. Also, we evaluate the effectiveness of the Metropolis-Hastings-based atomic valency modification and the extrapolation performance, which is important to material discovery.

Citations

• Semi-Supervised Learning with Deep Generative Models
• Conditional Image Synthesis With Auxiliary Classifier GANs
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
• SchNet – A deep learning architecture for molecules and materials
• Learning Latent Subspaces in Variational Autoencoders
• Large Scale GAN Training for High Fidelity Natural Image Synthesis

Cited by

• Generative AI for Crystal Structures: A Review

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