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Cited by 24Machine Learning in Materials ScienceComputational Drug Discovery MethodsCrystal Structures and Properties

Generative AI for crystal structures: A review

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De Breuck, Pierre-Paul, Wang, Hai-Chen, Rignanese, Gian-Marco, Botti, Silvana, Marques, Miguel A. L.·2025·npj Computational Materials
DOI 10.1038/s41524-025-01881-2
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Abstract

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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  • Other reviews concentrate on natural-language processing in materials science, large language models for materials and chemistry, reticular chemistry, or generative crystal-structure modeling.

    “Other reviews concentrate on natural-language processing in materials science, large language models for materials and chemistry, reticular chemistry, or generative crystal-structure modeling [5,11-14].”

    From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [14]

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Authors
De Breuck, P.-P., Wang, H.-C., Rignanese, G.-M., Botti, S., & Marques, M. A. L.
Affiliations
  • Research Center Future Energy Materials and Systems (DE)
  • UCLouvain (BE)
ORCID
Venue
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Published
2025-12-06
Volume
11
Issue
1
ISSN
2057-3960 (ISSN-L)
DOI
10.1038/s41524-025-01881-2
OpenAlex
W4417070768
Cited by
24
References
114
Citations by year
2026: 18 · 2025: 4 · 2023: 1 · 2022: 1
FWCI
1.09
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Computational Materials (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DIAL (Catholic University of Leuven) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Crystal Structures and Properties
Keywords
generative AI, crystal structure generation, materials discovery, generative models, model taxonomy, data representation, property prediction, materials informatics

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