Generative AI for Crystal Structures: A Review
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916930765455360 |
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| author | De Breuck, Pierre-Paul Wang, Hai-Chen Rignanese, Gian-Marco Botti, Silvana Marques, Miguel A. L. |
| author_facet | De Breuck, Pierre-Paul Wang, Hai-Chen Rignanese, Gian-Marco Botti, Silvana Marques, Miguel A. L. |
| contents | As in many other fields, 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 begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architecture, representation, conditioning, and materials domain to categorize the diverse range of current generative AI models. We discuss data sources and address challenges related to performance metrics, emphasizing the need for standardized benchmarks. Specific examples and applications of novel generated structures are presented. Finally, we examine current limitations and future directions in this rapidly evolving field, highlighting its potential to accelerate the discovery of new inorganic materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Crystal Structures: A Review De Breuck, Pierre-Paul Wang, Hai-Chen Rignanese, Gian-Marco Botti, Silvana Marques, Miguel A. L. Materials Science As in many other fields, 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 begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architecture, representation, conditioning, and materials domain to categorize the diverse range of current generative AI models. We discuss data sources and address challenges related to performance metrics, emphasizing the need for standardized benchmarks. Specific examples and applications of novel generated structures are presented. Finally, we examine current limitations and future directions in this rapidly evolving field, highlighting its potential to accelerate the discovery of new inorganic materials. |
| title | Generative AI for Crystal Structures: A Review |
| topic | Materials Science |
| url | https://arxiv.org/abs/2509.02723 |