MatterGen: a generative model for inorganic materials design

The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generating entirely novel materials given desired property constraints. Despite recent progress, current generative models have low success rate in proposing stable crystals, or can only satisfy a very limited set of property constraints. 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. To enable this, we introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice. We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset. Compared to prior generative models, structures produced by MatterGen are more than twice as likely to be novel and stable, and more than 15 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. Finally, we demonstrate multi-property materials design capabilities by proposing structures that have both high magnetic density and a chemical composition with low supply-chain risk. We believe that the quality of generated materials and the breadth of MatterGen’s capabilities represent a major advancement towards creating a universal generative model for materials design.

Research Forum 2 | Keynote: The Revolution in Scientific Discovery

Microsoft Research Forum | Episode 2 | March 5, 2024 Chris Bishop shared the vision for how AI for science will leverage AI to model and predict natural phenomena, including the exciting real-world progress being made by the team. See more at (opens in new tab)

Unlocking Real world solutions with AI – Chris Bishop

Chris Bishop reveals how AI is revolutionizing material science with an innovative battery electrolyte material. With the help of MatterGen, an AI system akin to a search engine, researchers can explore novel material options with precision and efficiency. The broad potential of these AI systems spans industries from drug discovery to environmental science.

MatterGen: A Generative Model for Materials Design

Tian Xie introduces MatterGen, a generative model that creates new inorganic materials based on a broad range of property conditions required by the application, aiming to shift the traditional paradigm of materials design with generative AI.