Metal-Organic Framework (MOF) Design using AI refers to the application of machine learning to predict the properties of and design novel metal-organic frameworks—crystalline porous materials composed of metal nodes connected by organic linkers—for applications in gas storage, separation, catalysis, and sensing. AI methods screen the vast combinatorial space of possible MOFs (>millions of hypothetical structures) to identify optimal candidates for specific applications.

Why MOF Design AI Matters in AI/ML: MOFs represent a uniquely AI-amenable materials design challenge because their modular construction (metal node + organic linker + topology) creates a massive combinatorial design space that is impossible to explore experimentally but naturally suited to ML-guided search and generative design.

• Property prediction from structure — GNNs and 3D convolutional networks predict gas adsorption capacities (CH₄, CO₂, H₂), selectivities, surface areas, and pore volumes from MOF crystal structures; models like MOFNet and CGCNN achieve accuracy within 10-15% of molecular simulation • Textual/tabular descriptors — Beyond graph representations, MOF properties correlate with geometric descriptors (pore limiting diameter, largest cavity diameter, surface area, void fraction) and chemical descriptors (metal type, functional groups, linker length) that serve as efficient ML features • Generative MOF design — VAEs and GANs generate novel linker molecules, and combinatorial enumeration with ML screening identifies promising metal-linker-topology combinations; inverse design methods specify desired properties and generate MOF structures to match • High-throughput screening — Databases like CoRE MOF, hMOF, and ToBaCCo contain 100K+ real and hypothetical MOF structures with computed properties; ML models trained on these databases enable rapid screening of the entire MOF chemistry space • Multi-objective optimization — Real MOF applications require balancing competing objectives: high gas uptake vs. easy regeneration, high selectivity vs. high capacity, stability vs. porosity; Pareto optimization identifies the optimal MOF candidates

MOF design AI exemplifies how machine learning transforms combinatorial materials discovery, enabling rapid exploration of the vast metal-linker-topology design space to identify optimal porous materials for gas storage, carbon capture, and catalysis applications that would require centuries of experimental trial-and-error without computational guidance.