FAIR-MOFs:A Comprehensive Database for Accelerating the Discovery and Synthesis of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are a versatile class of materials with applications in gas storage, separations, and catalysis. Despite extensive research, one of the key factors hindering their broader deployment is the absence of reproducible and scalable synthetic protocols. To address this, we introduce FAIR-MOFs, a database designed to be Findable, Accessible, Interoperable, and Reusable (FAIR), comprising 45{,}700 curated experimental structures, 33{,}361 geometry-optimised structures, and 4{,}161 entries linked to reported synthesis conditions. Analysis of the dataset showed that the propensity of open-metal sites in MOFs is statistically associated with reaction temperature, metal salts, ligands, topology, and metal secondary building unit. Furthermore, we developed a retrosynthetic recommender that captures literature co-usage patterns among solvents, metal salts, and ligands. For any given component, the system suggests compatible reagents and retrieves MOFs prepared under similar conditions. Finally, we trained a graph-based neural network integrated with our MOF deconstruction module to predict most probable metal salts, ligands and solvents directly from 3D structures of experimental or hypothetical MOFs. Using this model, we successfully synthesised MOFs randomly selected from hypothetical MOF databases illustrating the potential of FAIR-MOFs to accelerate the discovery and enable data-driven synthesis of MOFs.