We have built an active-learning model for predicting CO 2 and N 2 adsorption free energy. The training data was extracted by fitting a model functions to the GCMC-simulated adsorption isotherms, see Methods section. The active learning workflow uses extensive featurization combining multiple tools. In short, feature selection is performed by combining Lasso regression and variable importance discerned from initial machine-learning (ML) models. Multiple ML models are built using the selected features and free energies of adsorption, see Figure . The best-performing model with the lowest uncertainty is used for Pareto-searching higher-performing materials for subsequent iterations. The material selection improves CO 2 vs. N 2 selectivity by increasing ∆G N 2 -∆G CO 2 , as well as by selecting materials with lower prediction uncertainty. Further details are provided in the Methods section Overall, 982 materials were subjected to GCMC simulations over 5 active learning iterations. The progression of material selectivity is shown in Figure . The best performing material in each iteration is depicted by solid symbols, showing stepwise selectivity improvements. As a result of the active learning process that targets CO 2 selectivity, the distribution successively shifts towards structures with narrower pores, see Figure . Most materials in iterations 4 and 5, respectively, are MOFs having a pore-limiting diameter close to 2.5 Å. We note that for structures with pore dimensions similar to the size of the gas molecules, mass transfer is controlled by micro-pore diffusion .