Machine learning (ML) is a promising data-centric approach for prediction of gas permeabilities by learning a functional model based on polymer chemistry . ML methods using chemical inputs have been successfully applied to accurately predicting many polymer properties including glass transition temperature , thermal conductivity , dielectric constants , organic photovoltaic properties , and transport properties . The primary challenge for learning a generalizable ML model is training on robust and diverse data, which requires compiling multiple databases with the most recent literature values and imputing missing values . While Barnett et al. have trained accurate ML models that link polymer chemistry to gas permeability , their training set notably lacks PIMs, and they only screened a limited chemical space of 11,000 existing homopolymers. Therefore, ML-approaches would benefit from considering an expanded chemical space, such as additional training data on PIMs. Overall, ML-directed molecular design of polymer membranes still faces significant challenges in the following aspects: (1) How can we define an appropriate chemical space to explore the molecular design of high-performance polymer membranes? (2) Even if ML models can be established for the gas permeability prediction of polymer membranes, how can we achieve a physical understanding of how membrane chemistry affects gas separations? (3) Can we exceed the Robeson upper bound simultaneously for separations of different gas pairs, such as O2/N2, CO2/CH4, CO2/N2, and H2/CO2? To tackle the above challenges, we demonstrate interpretable, supervised ML models that can accurately predict the He, H2, O2, N2, CO2, and CH4 permeabilities of gas separation membranes based on polymer chemistry-as part of our ML-assisted discovery workflow outlined in Fig. .