This review surveys generative artificial intelligence applications for crystal structures in materials discovery, highlighting novel models and methods.
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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Breuck et al. (2025) studied this question.
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