Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.

Gas diffusion determines the performance of metal-organic frameworks (MOFs) in various practical applications, including membrane-based separations, yet its experimental measurement is challenging. We presented an efficient computational framework that integrates high-fidelity molecular dynamics (MD) simulations with machine learning (ML) to predict the diffusivities of CO2, N2, O2, CH4, and H2 in >18,000 synthesized and hypothetical MOFs. ML models trained on MD data accurately predicted gas diffusivities of any given MOF within minutes using only easily accessible structural and guest-related properties. We provided an interactive, user-friendly web interface for predicting diffusivities of MOFs to facilitate material selection. Leveraging ML-predicted diffusivities, we evaluated membrane-based gas separation performances of all MOFs for seven industrially important separations: CO2/N2, CO2/CH4, N2/CH4, H2/CO2, H2/CH4, H2/N2, and O2/N2. The best MOF membranes offering high selectivity and permeability were identified and analyzed by using molecular fingerprinting to reveal the critical chemical properties for designing next-generation MOFs.