We next repeated the analysis of transferability for CO2 working capacity. The RF regressor models trained to predict CO2 working capacity in USMOF DB fail to make accurate In USMOF DB, a considerable number of MOFs exhibit a preference for CH4 over CO2, while the majority of MOFs in CoRE MOF 2019 (89.4%) demonstrate a higher working capacity for CO2 over CH4. To further investigate this trend, we classified MOFs into three groups based on the working capacity ratio (see Sec. 3a). Then, we trained a RF regressor model on the set of MOFs with a high ratio of CO2:CH4 working capacity (i.e., > 1.1) and a low CO2:CH4 ratio (i.e., < 0.9) to analyze which features contribute the most to the different preferences on adsorbing gas molecules. We omitted the intermediate set with similar working capacities for both species to ensure that the models were learning from distinct sets of MOFs with significant preferences for one or the other molecule. The performance of models predicting CH4 working capacity trained on each set was marginally reduced (test R 2 = 0.97 and 0.98 for high-and low-ratio models, respectively) with respect to the model trained on the whole mechanically stable subset of USMOF DB, but otherwise was mostly comparable (Supporting Information Table ). The Gini impurity feature importance analysis reveals that both models emphasize gravimetric pore volume (i.e., the probe accessible volume and pore volume) and gravimetric surface area to predict the working capacity (Supporting Information Table ). Despite the similarities in the feature importance among models predicting CH4 working capacity for each set, the transferability between MOFs with high ratios and low ratios is notably poor. The R 2 for the model trained on MOFs with low ratios to predict those with high ratios is 0.18 and it is -0.70 for the reverse case. Even though these models share similar important features, the different range of CH4 working capacity for each set of MOFs limits model transferability (Supporting Information Figure ). Along with the RF model, we also investigated a LASSO model in this case since linear model can perform in a superior fashion in extrapolation when both models share similar important features. As expected, the R 2 for the model trained on low ratios to predict high ratios is 0.94 and 0.45 for the reverse case, showing improved accuracy of LASSO compared to RF (Supporting information Table ).