A generative model for inorganic materials design

Abstract

The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture^1–3. Generative models provide a new paradigm for materials design by directly generating novel materials given desired property constraints, but current methods have low success rate in proposing stable crystals or can only satisfy a limited set of property constraints ⁴⁻¹¹. Here, we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. Compared to prior generative models ^4,12, structures produced by MatterGen are more than twice as likely to be novel and stable, and more than 10 times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, novel materials with desired chemistry, symmetry, as well as mechanical, electronic and magnetic properties. As a proof of concept, we synthesize one of the generated structures and measure its property value to be within 20 % of our target. We believe that the quality of generated materials and the breadth of MatterGen’s capabilities represent a major advancement towards creating a foundational generative model for materials design.