At first, we tried to directly reproduce the computed activation energies with the predictive models trained on our in-house assembled HAT dataset, but even our best model (ensembled FFNN) reached an inadequate MAE of 4.58 kcal/mol (RMSE = 5.11 kcal/mol, R 2 =0.05). A reasonable explanation for this poor performance is (once again) the distribution of the data: our in-house generated dataset contained no examples involving HO • or HOO • radicals, and the activation energies for the reactions extracted from RMechDB (mean ∆G ‡ corr = 9.10 kcal/mol) tend to be at the lower edge of the activation energy distribution of our training data (cf. Figure ). Once The observations for this last application are yet another illustration that setting up a machine learning model for chemical reactivity, able to generalize across application domains, is hard and data-intensive, but that introducing QM-based descriptors in the model architecture -particularly in combination with transfer learning -can remedy this situation to a large extent.