As shown in Figure , active learning modeling is done by combining extensive featurization with iterative material selection. The featurization workflow combines multiple independent featurizers such as MAGPIE , JARVIS , persistent homology , CGCNN , synthesis complexity score (SCS) , Zeo++ 41 and PoreBlazer . As a fist step of the iterative materials section process, feature selection is carried out to reduce the feature space. This is done using combination of Lasso regression, variable contribution from random forest ensembles and shapely metrics. The reduced feature space is used to predict adsorption free energies ∆G for CO 2 and N 2 . We employ an ensemble of models to predict the adsorption performance. Our ML modeling tool box comprised of GPR, RFE, SVM, PLS, NN with details given in section S1. The best model is selected based on performance on the test dataset, as well as the prediction uncertainty over the entire database. The selected model is used to Pareto-search and select the next set of materials to be subjected to detailed GCMC simulations. The Pareto search is performed to minimize ∆G CO 2 and maximize ∆G N 2 . Within each Pareto front the selected materials are sorted and filtered based on synthesis complexity score and the model's prediction uncertainty for adsorption free energy. We carried out 5 such active learning iterations, each including: detailed molecular simulation, (re-)building machine learning models to predict adsorption performance and material selection for subsequent iterations.