  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.
