Generative AI for crystal structures: a review
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.
Authors
- Hai‐Chen Wang (ORCID: https://orcid.org/0000-0002-2892-5879)
- Miguel A. L. Marques (ORCID: https://orcid.org/0000-0003-0170-8222)
- Silvana Botti (ORCID: https://orcid.org/0000-0002-4920-2370)
- Gian‐Marco Rignanese (ORCID: https://orcid.org/0000-0002-1422-1205)
- Pierre-Paul De Breuck
Institutions
- Walloon Excellence in Lifesciences and Biotechnology (BE)
- Ruhr University Bochum (DE)
- UCLouvain (BE)
Publication Details
- Journal
- npj Computational Materials
- Published
- 2025-12-06
- DOI
- https://doi.org/10.1038/s41524-025-01881-2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00