The overall model architecture consists of four EGAT layers that yield compressed fingerprints of the reactant(s) and product(s), which are then used to predict the E a using a feed-forward stack (Fig. ). Even though there are four total EGAT layers, only two are distinct, with the first being unique and the last three sharing weights. The first is responsible for embedding the raw node and edge features in a higher dimensional space so it has different internal dimensions from the other three. The three-fold application of the second EGAT layer results in mixing of information 1. Four of the vector values determine the order of the bond, while the additional value determines whether the bond is aromatic. 2. One of the bits denotes whether there is a bond break, a second denotes whether a bond forms, a third denotes whether the bond order changes, a fourth denotes whether the bond is unchanged. The model consists of a stack of edge-featured graph attention layers (green and red) that generate the derived features for each edge and node in the reactant and product. The differences in these features are then transformed and pooled to generate a global fixed-size reaction fingerprint (purple) that is used as an input for the E a prediction task (gray). Depending on the formulation of the learning task, the final predictor may be trained to solely predict E a , E a and ∆H r in multi-task fashion, or α and β the linear free energy parameters associated with E a . The lower inset illustrates the edge-featured graph attention mechanism applied to a single node and its edges.