However, in Fig. (a), our ML models show that these groups tend to have a greater negative impact (measured by SHAP value) on N2 and CH4 permeability, compared to O2 and CO2, which explains why the presence of these groups in polyimides, ladder polymers, and poly(ethylene oxides) can increase selectivity by widening the permeability difference between certain pairs of gases, which is desirable for gas separations. This supports a known heuristic in membrane design, that CO2 selectivity can be increased via increased CO2 solubility by incorporating oxygen atoms into polymer membranes . Across the board for substructures, SHAP values tend to be higher for N2 and CH4 compared to O2 and CO2 (Fig. ), which suggests that incorporating chemistries that increase permeability (i.e. methyl groups) is likely to come at the cost of selectivity. Our ML models thus elucidate a chemical basis for the permeability/selectivity tradeoff: chemical features that increase permeability are likely to do so to a greater extent for molecules that are less permeable (N2 and CH4), but chemical features that reduce permeability are also likely to impact these molecules to a greater magnitude-thereby increasing selectivity for the more permeable gas (O2 and CO2). Achieving high permeability and selectivity thus becomes a balancing act. This unique understanding is unlocked from the ability of ML to learn complex patterns in data.