FAIR-MOFs: Structure-centred synthesis inference from three-dimensional structures of metal-organic frameworks

Metal-organic frameworks (MOFs) hold great promise for applications ranging from CO2 capture to atmospheric water harvesting, yet their discovery is hindered by limited access to reproducible and scalable synthesis. Here, we introduce FAIR-MOFs, a data-driven framework that directly links 3D crystal structures to experimentally validated synthetic routes. FAIR-MOFs combines a graph neural network that predicts essential synthesis precursors from structure alone with a retrosynthetic recommender that learns reagent co-occurrence patterns from literature. The framework is built on over 47,000 curated experimentally synthesised structures integrated through automated structural curation and building-unit recognition to maintain data integrity and FAIR principles. We demonstrate its predictive power by experimentally synthesising three hypothetical MOFs using conditions proposed by the model. Overall, FAIR-MOFs introduces a structure-centred approach to synthetic planning by demonstrating that synthetic precursors can be inferred directly from three-dimensional crystal structures of metalorganic frameworks.