Our training data consists of polymer chemistry and experimental gas permeabilities from two large databases, PolyInfo and Membrane Society of Australasia (MSA) , for hundreds of homopolymers, including PIMs. We utilize two representations for the polymer repeating unit, namely chemical descriptors as generated by RDKit (listed in Table of Supporting Information) and the Morgan fingerprint with frequency (MFF) . We impute missing permeabilities using multivariable imputation by chained equations (MICE) , and we then train multitask supervised ML models to establish synthesis-property relations for these polymer membranes. While various supervised ML models have been used in polymer informatics, including recurrent neural networks, support vector machines, gaussian processes, and others, we choose to focus our study to random forest (RF) regression and deep neural networks (DNN), which have demonstrated outstanding performance in our recent benchmark study . Due to the high variance of DNNs, we perform bootstrap ensembling to further improve our predictions (achieving testing R 2 ~0.90). We also interpret our ML models by extracting feature importances using SHAP (SHapley Additive exPlanations) analysis. Our analysis provides a chemical explanation for the well-known permeability-selectivity tradeoff in membranes, and many of the other physical insights that we draw are consistent with established membrane design principles.