Pan, EltonKwon, SoonhyoungLiu, SulinXie, MingrouHoffman, Alexander J.Duan, YifeiPrein, ThorbenSheriff, KillianRoman-Leshkov, YuriyMoliner Marin, ManuelGomez-Bombarelli, ROlivetti, Elsa A.2026-03-232026-03-232026-022662-8457https://riunet.upv.es/handle/10251/233543[EN] The synthesis of crystalline materials, such as zeolites, remains a notable challenge owing to a high-dimensional synthesis space, intricate structure-synthesis relationships and time-consuming experiments. Here, considering the 'one-to-many' relationship between structure and synthesis, we propose DiffSyn, a generative diffusion model trained on over 23,000 synthesis recipes that span 50 years of literature. DiffSyn generates probable synthesis routes conditioned on a desired zeolite structure and an organic template. DiffSyn a chieves state-of-the-art performance by capturing the multi-modal nature of structure-synthesis relationships. We apply Diffsny to differentiate among competing phases and generate optimal synthesis routes. As a proof of concept, we synthesize a UFI material using DiffSyn-generated synthesis routes. These routes, rationalized by density functional theory binding energies, resulted in the successful synthesis of a UFI material with a high Si/AlICP of 19.0, which is expected to improve thermal stability.Reserva de todos los derechosZeolitesSynthesis relationshipsCrystalline materialsArtículo10.1038/s43588-025-00949-9Embargado41629624