Next, we carried out feature importance analysis on the RF regressor model trained to predict the CH4 working capacity of mechanically stable MOFs in USMOF DB or the MOFs in CoRE MOF 2019 (Supporting Information Figure ). The impurity-based feature importances , which we calculated for both databases, underscore the significance of geometric features over chemical features in predicting the CH4 working capacity. Notably, the gravimetric pore volume emerged as the most important feature, with a feature importance of 89.9% for USMOF DB and 94.7% for CoRE MOF 2019, highlighting how MOFs with large pore volumes can accommodate more CH4 gas molecules (Supporting Information Figure ). The smaller, spherical size and absence of dipole and quadrupole moments in the CH4 molecule restrict its interaction with framework atoms, resulting in a minor contribution from chemical features. This aligns with the Chahine rule where hydrogen adsorption is proportional to the BET surface area. Since MOFs in USMOF DB have large pore volumes, CH4 adsorption is proportional to pore volume rather than surface area (Figure ). For example, MOF-399 (CSD 38 refcode: BAZGAM) has been identified previously in the literature 102 for its exceptional gas uptake, but its relatively large gravimetric pore volume of 7.39 cm 3 /g is eclipsed by a hypothetical MOF in USMOF DB with Mg-based inorganic node (N54) and long organic edge (E13), which is derived from [p-terphenyl]-4,4"-dicarboxylic acid (H2TPDC, see discussion in Sec. 3c) that possesses a large gravimetric pore volume of 13.67 cm 3 /g and exceptionally high CH4 working capacity of 1141.40 cm 3 /g (Figure and Supporting Information Figure ). Thus, there is a relatively consistent emphasis on pore volume to predict CH4 uptake when models are trained on either dataset, but the pore sizes are even larger in the mechanically stable subset of USMOF DB than in previously synthesized CoRE MOF 2019 MOFs. In comparison to CH4, the CO2 molecule possesses a non-zero quadrupole moment and a larger size with a linear shape. Feature importance analysis for the model predicting CO2 working capacity of MOFs in CoRE MOF 2019 highlights the significance of geometric features, particularly gravimetric pore volume with a feature importance percentage of 89.3% for predicting CO2 working capacity (Supporting Information Figure ). Conversely, for the mechanically stable but large-pore MOFs in USMOF DB, both the geometric features including surface area (40.0% importance) and chemical RACs, specifically metal-centered electronegativity RACs (17.3% importance), play crucial roles (Supporting Information Figure ). Across CoRE MOF 2019, there is a stronger correlation between CO2 working capacity and gravimetric pore volume (R 2 = 0.66) while the mechanically stable MOFs in USMOF DB do not follow this trend as strongly (Figure ). This difference between the feature importance of two databases can be attributed to the different ranges of gravimetric surface area values (Supporting Information Figure ). With a smaller range of gravimetric surface area values in CoRE MOF 2019, specific host-guest interactions do not play as important a role as they do in the mechanically stable USMOF DB subset. In the mechanically stable MOFs in USMOF DB, large surface areas may be coupled to specifically favorable adsorption sites, both contributing to adsorption trends.