A generative model for inorganic materials design

Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka () and Tian Xie ()
Additional contact information
Claudio Zeni: Microsoft Research AI for Science
Robert Pinsler: Microsoft Research AI for Science
Daniel Zügner: Microsoft Research AI for Science
Andrew Fowler: Microsoft Research AI for Science
Matthew Horton: Microsoft Research AI for Science
Xiang Fu: Microsoft Research AI for Science
Zilong Wang: Chinese Academy of Sciences
Aliaksandra Shysheya: Microsoft Research AI for Science
Jonathan Crabbé: Microsoft Research AI for Science
Shoko Ueda: Microsoft Research AI for Science
Roberto Sordillo: Microsoft Research AI for Science
Lixin Sun: Microsoft Research AI for Science
Jake Smith: Microsoft Research AI for Science
Bichlien Nguyen: Microsoft Research AI for Science
Hannes Schulz: Microsoft Research AI for Science
Sarah Lewis: Microsoft Research AI for Science
Chin-Wei Huang: Microsoft Research AI for Science
Ziheng Lu: Microsoft Research AI for Science
Yichi Zhou: Microsoft Research AI for Science
Han Yang: Microsoft Research AI for Science
Hongxia Hao: Microsoft Research AI for Science
Jielan Li: Microsoft Research AI for Science
Chunlei Yang: Chinese Academy of Sciences
Wenjie Li: Chinese Academy of Sciences
Ryota Tomioka: Microsoft Research AI for Science
Tian Xie: Microsoft Research AI for Science

Nature, 2025, vol. 639, issue 8055, 624-632

Abstract: Abstract The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture1–3. Generative models accelerate materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only a limited set of property constraints4–11. 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 with previous generative models4,12, structures produced by MatterGen are more than twice as likely to be new and stable, and more than ten times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, new materials with desired chemistry, symmetry and 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 abilities of MatterGen represent an important advancement towards creating a foundational generative model for materials design.

Date: 2025
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DOI: 10.1038/s41586-025-08628-5

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