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DOE OSTI · 2563750

Facilitating Screening of MOFs for Mixed Matrix Membranes Using Machine Learning and the Maxwell Model

Yu, Xiaohan [Georgia Institute of Technology, Atlanta, GA (United States)]·Chng, Jia Yuan [Georgia Institute of Technology, Atlanta, GA (United States)]·Sholl, David S. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000227719168)
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Abstract

Metal organic framework (MOF)-based mixedmatrix membranes (MMMs), which embed MOF particles in polymer matrices, combine the advantages of polymeric and inorganic membranes. Multiple previous studies have used the Maxwell model together with molecular simulations and machine learning (ML) to predict the performance of MOF/polymer MMMs. However, the assumption of rigid MOF frameworks in molecular simulations limited the accuracy of the data used in the predictions, particularly in predicting molecular diffusivities. We developed a novel workflow integrating ML models with consideration of MOF flexibility to predict the permeability and selectivity of 131,722 MMMs for CO 2 /CH 4 , O 2 /N 2 and He/H 2 separations. The full range of achievable MMM performance within the Maxwell model was analyzed, and several promising MOFs were identified using this workflow. This approach offers an efficient tool for screening any polymer and MOF combination in gas separation applications.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYDiffusionMetal organic frameworksPermeabilityPolymersSelectivitydiffusionmetal organic frameworkspermeabilitypolymersselectivity

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Yu, Xiaohan [Georgia Institute of Technology, Atlanta, GA (United States)], Chng, Jia Yuan [Georgia Institute of Technology, Atlanta, GA (United States)], Sholl, David S. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000227719168). 2025-05-01. Facilitating Screening of MOFs for Mixed Matrix Membranes Using Machine Learning and the Maxwell Model. https://doi.org/10.1021/acs.jpcc.5c01483

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