We conducted a similar analysis on each set of MOFs for CO2 working capacities. The performance of CO2 models trained on high-and low-ratio subsets of the mechanically stable subset of USMOF DB actually improved (test R 2 = 0.89 and 0.84 for high and low ratio, respectively) over the model trained on the entire dataset despite having smaller training set. The RF model trained on MOFs with a high CO2:CH4 ratio emphasizes gravimetric surface area (feature importance of 28.2%) and connectivity RACs (f-T-0-all and f-T-1-all, in which T describes the number of atoms to which an atom is bonded, with feature importance of 15.4% and 5.4% respectively, Supporting Information Table ). On the other hand, the model trained on MOFs with the low-ratio materials predominantly emphasizes the gravimetric surface area (feature importance of 79.2%). These models trained on each subset of the dataset highlight that different features contribute to the CO2 working capacities between the MOFs with high preference for CH4 and those that prefer CO2. Therefore, it is expected that these models should fail to transfer from high to low ratio or vice versa. The RF model applied to the other subset (i.e., high to low) shows test R 2 values of -30.43 (-0.12 for low to high), indicating that distinct features are employed to predict the CO2 working capacity of the two sets even when the distributions overlap (Supporting Information Table ). The unexpected disparities in working capacities between the mechanically stable subset of the USMOF DB and CoRE MOF 2019 indicate that even though USMOF DB is derived from CoRE MOF 2019 building blocks, recombining the building blocks with varying connectivity can substantially alter the working capacity. This suggests there are opportunities to tune preferences of MOFs toward CH4 or CO2 adsorption that have not yet been explored experimentally.