Then, we calculate the descriptors summarized below (and fully detailed in SI) on this restrained dataset. At this stage, 140 structures fail in the GCMC calculation due to RAM limitations and 83 have no standard deviation for the pore distribution (skewness and kurtosis cannot be retrieved). In the following training-testing process, we will therefore use a final dataset of 8 300 structures used to perform our ML-assisted method of screening the Xe/Kr adsorption selectivity. Based on this final set, the model is trained on 80% of randomly selected structures (6 640 structures) and tested on the remaining structures (1 660 structures). However, Jablonka et al. have recently criticized the standard train/test split (as used in this study) because of the multiple occurrences of similar structures in the data. Therefore, we also compare the results obtained by the GroupShuffleSplit function of sklearn.