We then train the final model on the predefined training set using XGBoost with the finetuned hyperparameters. By testing it on the test set, we measure the accuracy of our approach, however, it is interesting to extract chemical insight into the hidden relationship between the predicted value and the descriptors, to apprehend the thermodynamic origins of the performance. In this work, we use the Shapley values, 42 a game theory concept developed by Shapley in 1953, to measure the contribution of each descriptor in the predicted value. We can evaluate, locally on a nanoporous material, the contribution of each descriptor to the prediction using this tool. To draw structure-property relationships, we would need to use a global interpretation methods such as the SHapley Additive exPlanations (SHAP) method thoroughly detailed in the online book Interpretable Machine Learning of Christoph Molnar. The SHAP tool developed by Lundberg and Lee 44 is a faster algorithm adapted to tree-based ML models like gradient boosting, TreeSHAP, which allows the calculation on large databases and integrates useful global interpretation modules like feature importance evaluation and dependence plot.