Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials

Understand

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.