To assess our expectations of generality based on feature importance commonalities and differences, we evaluated the transferability of the ML models between databases for CH4 and then for CO2. As expected, the RF model trained on the USMOF DB, which covers a broader range of working capacity values, exhibits high performance on CoRE MOF 2019, achieving a test R 2 of 0.76 due to the overriding importance of gravimetric pore volume in predicting CH4 working capacity for both databases (Supporting Information Figure ). However, despite the fact that an RF model trained on CoRE MOF 2019 demonstrated excellent performance (test R 2 = 0.97) in predicting CH4 working capacity, the same model encountered challenges in accurately predicting the CH4 working capacity of mechanically stable USMOF DB MOFs, yielding a test R 2 of -0.18. Specifically, the prediction is accurate for the working capacity range in the domain of the model's training data (0 to 300 cm 3 (STP)/g framework) but shows a systematic underestimation over the range greater than the training data (> 300 cm 3 (STP)/g framework). The limited transferability from CoRE MOF 2019 to the mechanically stable subset of USMOF DB stems from the absence of data points with high CH4 working capacity in the former database that are present in the latter (Supporting Information Figure ). The nature of a RF regressor prediction, where predictions are produced by averaging the predictions of the decision trees in the forest, causes the model to fail in extrapolation, resulting in underestimations in this case. Nevertheless, few, if any, alternative regression models would be expected to extrapolate in a reliable fashion, highlighting the importance of generating data beyond the range of values in synthesized MOFs in order to predict properties of MOFs with exceptional characteristics beyond previously studied materials.