The data was then used to train ML models, with the inputs being the atomic numbers and coordinates of each structure (which are the same as the xTB-optimized geometries in tmQM, after centering). Edges were computed on-the-fly during training, and the targets were the reference corrected electronic energies. The same four GNNs as before were trained (SchNet, SpinConv, PaiNN, and GemNet-T ). This resulted in 32 models being trained, from 8 different splits to choose from and 4 different models. Configuration files for all trainings conducted are included in the supporting code. Just as with tmQM, these models were trained from scratch with data normalization, and used MAE as their loss metric.