金属有機構造体におけるガス拡散係数および透過係数の正確な予測のための分子モデリングベースの機械学習

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.