Pierre-Paul De Breuck, Hai‐Chen Wang, Gian‐Marco Rignanese, Silvana Botti, Miguel A. L. Marques
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.