For each of these descriptors, we train two Keras deep neural network (DNN) architectures using ChemML [85] -1) for predicting the ∆G of individual reaction steps, and 2) for predicting the ∆G of all reaction steps simultaneously. For the first architecture, the input descriptors correspond to the catalyst-substrate adduct structures, whereas for the second architecture, they only take into account the catalyst structures.