To justify the use of an arguably complex graph neural network, we compared it to two simpler methods, a densely connected feed-forward neural network and a random forest model (as implemented in scikit-learn , see ESI † ). For both methods, we used the L-MBTR descriptor as input and the barriers computed for non-optimized reactions as targets. Details on the network architecture and input generation are given in the Methods section. Using an ensemble of ten models, the feed-forward neural network accomplishes an MAE of 4.8 kcal/mol, and single models achieve 5.1 kcal/mol on average over trajectory data (see Figure ). The random forest model achieved an MAE of 6.6 kcal/mol (Figure ). Taken together, these results highlight the need for more sophisticated representations and models to capture subtle structural differences. However, it is important to note that our model significantly outperforms semi-empirical methods, in both accuracy and speed. More specifically, we compared barriers calculated with the cheaper GFN2-xTB method to BMK-calculated ones on 100 randomly chose structures. xTB calculations of the barrier do not reach the needed quality as they differ to BMK calculated barriers by an MAE of 20 kcal/mol (Figure ).