The search for hyperparameter values aims at finding the best model to optimize the generalization error. The most common strategy is to perform cross-validations to evaluate different model configurations, known as hyperparameter search or optimization. In this case, the randomized search algorithm with 5-fold cross-validation is used to find the best parameters within a predefined reasonable range. The Supporting Information provides the range of hyperparameters explored by the algorithm. After this search, we identify a set of optimal hyperparameters, which gives an average RMSE of 0.37 kJ mol -1 , which defines our final model. A convergence plot of the model performed using 5-fold cross-validations is given in Given this configuration, we test the model on the test set and use interpretation tools to understand better the structure-property relationships in play.