Several variations of the Edge-featured Graph Attention Network 62 (EGAT) architecture are developed here for the prediction of activation energies from reactant and product graphs. The main elements shared by all the architectures are described in the Model Overview, Input Features, and Description subsections (Sections 2.1-2.3) and the differences between models are described in Learning Tasks subsection (Section 2.4). These models are compared with Chemprop, 63 a directed message passing neural network (D-MPNN) model that has previously been trained for the G2Ea task. The training and implementation details of Chemprop in this work followed those supplied by the developers through their distributed code, additional can be found in the SI (Section 2).