Demir, HakanKeskin, S.2023-11-072023-11-072024-011438-7492http://hdl.handle.net/10679/8940https://doi.org/10.1002/mame.202300225Membrane-based separation can offer significant energy savings over conventional separation methods. Given their highly customizable and porous structures, metal–organic frameworks- (MOFs) are considered as next-generation membrane materials that can bring about high separation performance and energy efficiency in various separation applications. Yet, the enormously large number of possible MOF structures necessitates the development and implementation of efficient modeling approaches to expedite the design, discovery, and selection of optimal MOF-based membranes via directing the experimental efforts, time, and resources to the potentially useful membrane materials. With the recent developments in the field of atomic simulations and artificial intelligence methods, a new era of membrane modeling has started. This review focuses on the recent advances made and key strategies used in the modeling of MOF-based membranes and highlight the huge potential of combining atomistic modeling of MOFs with machine learning to explore very large number of MOF membranes and MOF/polymer composite membranes for gas separation. Opportunities and challenges related to the implementation of data-driven approaches to extract useful structure–property relations of MOF-based membranes and to produce design principles for the high-performing MOF-based membranes are discussed.enginfo:eu-repo/semantics/openAccessAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Review309100106807030000110.1002/mame.202300225Gas separationMachine learningMembranesMixed matrix membranesMOFsMolecular simulation2-s2.0-85171672162