To evaluate the representational validity of the selected training dataset, we conducted a comparative analysis using an alternative set of 611 MOFs with similar structural property distributions. This ensures that the trends observed in model training and prediction are not artifacts of a specific subset but rather generalizable across the broader MOF space. A detailed statistical comparison, including histograms, kernel density estimation (KDE) plots, and principal component analysis (PCA), is provided in the SI (Figures ). These analyses confirm that the alternative set preserves the overall structural characteristics of the original selection while consisting of distinct MOFs. This targeted approach led to a GP model with a good predictive capability, achieving an 𝑅² of 0.945 and a MAE of 5.21 cm³ (STP)/g framework, which are comparable to the original results from the 611 common MOFs (Figure ).