All descriptors are described in detail in Table , and were used to train the supervised ML model presented further in this article. The model architecture chosen is an XGBoost 33 framework, similar to that previously used in our study of the thermodynamics of Xe/Kr selectivity in nanoporous materials, and the hyperparameters of the XGBoost model were determined using a random search. The SHAP interpretation tools are applied to better understand the underlying reasons behind the performance of the final ML model.