Discovering metal-organic framework/polymer mixed-matrix membranes via machine learning for CO₂ separation

Mixed-matrix membranes (MMMs) that incorporate metal-organic frameworks (MOFs) as fillers surpass the permeability-selectivity trade-off of polymeric membranes. However, the enormous chemical diversity of MOFs and high computational cost of molecular simulations have hindered the systematic evaluation of large numbers of MOF/polymer MMMs. In this work, we present a data-driven discovery framework that integrates molecular simulations and machine learning (ML) to predict the CO₂, CH₄, N₂, and H₂ permeabilities of 104,196 different types of MOF/polymer MMMs. Molecular simulations were performed to obtain gas adsorption and diffusion properties for 3,982 synthesized MOFs and 4,701 hypothetical MOFs. Leveraging this dataset, we developed three ML approaches to enable rapid prediction of MMMs' permeabilities and benchmarked their performance against both simulation results and experimental measurements. Our results revealed that many MOF/polymer MMMs exceed the upper bounds by achieving very high CO₂ permeabilities and MOFs' structural and chemical features play a decisive role in fine-tuning MMM performance.