We perform a randomized search over a range of maximum depths, learning rates, sizes of feature samples used by tree and by level, sizes of data sample and alpha regularization parameters. And a set of hyperparameters minimize the average RMSE computed using a 5-fold cross-validation. The ranges used in the randomized search as well as the final hyperparameters set are given in the SI. By using this parameterization, our XGBoost model has an RMSE of 0.37 kJ mol - and an MAE of 0.22 kJ mol -1 on the exchange Gibbs free energies of the test set containing 1 660 structures, which corresponds to a good correlation as shown in Figure . If we convert back these results to the selectivity values, the RMSE on the selectivity values would be 2.5 and 0.07 on the log 10 of the selectivity, which means that the order of magnitude of the selectivity is known with a very good accuracy.