Machine learning has been successfully applied to predict structures, energies, and properties of molecules. However, for the prediction of kinetic quantities, machine learning only gained traction in the last years. Previous works often reported respectable accuracies below 1 kcal/mol, but relied on inputs derived from DFT calculations. While a low number of single point energy calculations are still less expensive than the otherwise required optimizations, ideally one would want to replace all DFT calculations at inference time, be it at the loss of some accuracy. And indeed, this has been attempted for, e.g., catalysis on metal surfaces and reactions between small molecules, reaching an MAE of 5 and 2.6 kcal/mol, respectively. One notable exception is the prediction of dihydrogen activation with Vaska's complex by co-author Friederich et al., where accuracies below 1 kcal/mol were achieved without the need for DFT calculated inputs, however over a very narrow range of 0-25 kcal/mol. Taken together, predicting reaction barriers by a surrogate model which Fig. Schematic of the workflow. The system of interest here is a collagen fibril under tension, containing radicals (red). A cutout of the fibril is presented to the neural network, which predicts the energy barrier E a for every possible reaction, one at a time. This information can be used to decide which reaction is likely to occur and continue the simulation after the reaction.