Generative AI for crystal structures: a review

De Breuck, Pierre-Paul;Wang, Hai-Chen;Rignanese, Gian-Marco;Botti, Silvana;Marques, Miguel A. L.
(2025) npj Computational Materials — Vol. 11, n° 1, p. 370 (2025)

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Authors
  • De Breuck, Pierre-PaulUCLouvain
    Author
  • Wang, Hai-ChenInterdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum, Bochum, Germany
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  • Rignanese, Gian-Marcoorcid-logoUCLouvain
    Author
  • Botti, SilvanaInterdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum, Bochum, Germany
    Author
  • Marques, Miguel A. L.Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University Bochum, Bochum, Germany
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Abstract
The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We outline architectures, representations, conditioning mechanisms, data sources, metrics, and applications, and organize existing models into a unified taxonomy.
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De Breuck, P.-P., Wang, H.-C., Rignanese, G.-M., Botti, S., & Marques, M. A. L. (2025). Generative AI for crystal structures: a review. npj Computational Materials, 11(1), 370. https://doi.org/10.1038/s41524-025-01881-2 (Original work published 2025)