For the prediction of HAT energy barriers in this work, we decided to use equivariant graph neural networks, as they performed best on previous tests compared to random forest regressors, dense NNs and non-equivariant NNs. This is representative of the overall development in representations of chemical systems for machine learning. Representations such as the Coulomb Matrix 18 perform well on simpler tasks, but are typically outperformed by more expressive representations, e.g., atom-centered symmetry functions, smooth overlap of atomic positions (SOAP), and, in the last years, graph neural networks (GNNs). The latest accuracy increases can be partially attributed to moving from invariant to equivariant models, where we arrive at the GNN used in this work, namely PaiNN. Originally, PaiNN was designed to predict global quantities like total energy, but as the reaction barrier does not scale with system size, we adjusted the architecture for the prediction of local quantities.