Data normalization was used during model training, with the mean and standard deviation of the targets computed ahead of time. Four GNNs were tested, namely SchNet, SpinConv, PaiNN, and GemNet-T. Configuration files for all trainings conducted are included in the supporting code. All weights were initialized randomly, i.e., the models were trained from scratch. All models used MAE as their loss metric, and were trained using performing checkpoint provided by the model (the weights used during the epoch with the lowest validation MAE) and predicting on the test set. In addition to MAE, models were evaluated by energy within threshold (EwT), the percentage of structures for which ML energy predictions were within 1 kcal/mol of the computed DFT energy. Parity plots were also generated, which assess whether or not the predictions are generally in line with the targets.