4. Homology: Persistent homology captures deep topological effects inherent to the pore structure of the material. It incorporates the ability of internal spaces within materials to expand or contract to occlude or accommodate other molecules in transport. They are therefore an alternative pseudo-dynamic way of capturing shape effects inherent to crystalline structures. We embed these shape effects into atom-specific features following the procedure outlined in for atom-specific persistent homology (ASPH). These features potentially reveal two-body and multi-body interactions in the homology. They are highly accurate predictors of formation energy . Every feature we use is calculated as a persistence barcode from a point-clouds of atomic neighborhoods. For more details see the Methods section of . There are 2,800 barcode features corresponding to 90 elements each with 35 barcodes related to minimum, maximum, mean, standard deviation and sum (x5) corresponding to 7 barcodes, namely, Betti-0, Betti-1 (start, stop and length) and Betti-2 (start, stop, length). The features generated from steps 1 through 4 are pooled and id-ed by their unique structural ID, to form the base feature pool for the rest of the active learning process. In that sense, the active learner is continuously forming nonlinear maps over subsets of features at every iteration based on new adsorption data. Every such new map informs the material selection for the next iteration In additional to above described features, there are additional 32 features related to electronic structure of the elements in the material, atomic volume, atomic wight, density, melting point etc. Each of these have min, max, mean, standard deviation and sum listed as separate features. In total the other features are 179.