# A generative model for inorganic materials design

“A generative model for inorganic materials design” (2025), by Claudio Zeni et al. Materials Science research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew T. Fowler, Matthew K. Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda et al.
- Published: January 16, 2025
- Field: Materials Science
- DOI: https://doi.org/10.1038/s41586-025-08628-5
- Free PDF: https://www.nature.com/articles/s41586-025-08628-5_reference.pdf

## Abstract

The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture 1–3 . 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 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 with previous generative models 4,12 , 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.

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