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

Dataset Description The following datasets constitute the FAIR-MOFs repository, which is a comprehensive and highly curated collection of metal-organic frameworks (MOFs), associated ligands, metal clusters, porosities, formation energies, synthetic conditions, and other derived features. These files can be directly downloaded and used for machine learning, data mining, cheminformatics, and synthesis prediction tasks. Structural Data FAIR-MOFs_exp.zipContains CIF files for 45,699 curated experimental MOF crystal structures. FAIR-MOFs_opt.zipContains CIF files for 33,361 geometry-optimised MOF structures. FAIR-MOFS_ligands_and_metal_cluster.gzContains all organic ligands and metal clusters obtained from systematic deconstruction of the curated MOFs. Porosity, OMS, and General Information oms_and_general_info_of_optimised_mof.jsonContains open-metal-site (OMS) information and general descriptors for geometry-optimised MOFs. oms_and_general_info_of_unoptimised_mof.jsonContains OMS and general information for unoptimised MOFs. porosity_data_of_optimised_mof.jsonPorosity data computed for all geometry-optimised MOFs. porosity_of_unoptimised_mof.jsonPorosity information for all unoptimised structures. Ligand and SBU Data ligands_data_of_optimised_mof.jsonStructural, chemical, and topological data for organic ligands in optimised MOFs. ligands_of_unoptimised_mof.jsonLigand information extracted from the unoptimised curated structures. metal_sbus_and_linkers_of_unoptimised_mof.jsonMetal SBUs (secondary building units) and linker fragments identified in unoptimised MOFs. sbus_and_linkers_of_optimised_mof.jsonMetal SBUs and linker data obtained from geometry-optimised MOFs. Synthetic Conditions and Chemical Metadata complete_experimental_synthetic_conditions.jsonIncludes complete detailed experimental synthesis conditions extracted for tens of thousands of MOFs. experimental_synthetic_conditions_used_for_gnn.jsonCurated synthesis conditions used as supervised learning targets for graph neural network (GNN) models. abbreviation_of_organic_chemicals.jsonMapping of common abbreviated chemical names to the full chemical names. solvent_abbreviation.json and solvent_to_inchi_and_smile.jsonProvide solvent abbreviation expansion and solvent structural identifiers (InChI & SMILES). mof_organic_reagents.jsonList of all organic reagents involved in MOF synthesis. updated_chemical_names_to_inchi_and_smiles.jsonStandardised structural identifiers for chemicals found in the synthesis database. Energy, Thermodynamic, and Bond Dissociation Energy compiled_bond_dissociation_energies_per_formular_unit_kj_mol.json: Bond dissociation energies mapped to ligand or SBU formula units. mof_formation_energy_sbu.json: Formation energies computed per metal–SBU unit. mof_formation_energy_ligand.json: Formation energies computed per organic ligand. Additional Metadata and Derived Features doi_refcode.jsonMapping between DOIs and crystallographic refcodes. categories_for_one_hot_encoding.json: Canonical categorical sets used for featurisation and supervised ML tasks. final_combined_data_for_analysis.json: Cleaned and merged dataset containing structural, chemical, synthesis, porosity, and energy features. DIO_2021_and_other_properties.csv: Contains a wide range of MOF descriptors used in large-scale property prediction benchmarks. Topology.csv: Structural topology assignments for MOFs. Training_data_for_sentiment_analysis.xlsx: Dataset used to classify synthesis-writing sentiment and text patterns.