  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 both
explicit chemical knowledge and implicit composition rules embodied in the
large materials database. Here, we propose a generative machine learning model
(MatGAN) based on a generative adversarial network (GAN) for efficient
generation of new hypothetical inorganic materials. Trained with materials from
the ICSD database, our GAN model can generate hypothetical materials not
existing in the training dataset, reaching a novelty of 92.53% when generating
2 million samples. The percentage of chemically valid (charge neutral and
electronegativity balanced) samples out of all generated ones reaches 84.5% by
our GAN when trained with materials from ICSD even though no such chemical
rules are explicitly enforced in our GAN model, indicating its capability to
learn implicit chemical composition rules. Our algorithm could be used to speed
up inverse design or computational screening of inorganic materials.
