After preprocessing, data was split between training, validation, and test sets. These splits were all done completely randomly. To assess the effects of increasing training dataset size on model performance, four different train/val/test splits were used, with 80/10/10, 60/20/20, 40/30/30, and 20/40/40 splits. All of these splits are included in the supporting code. There were eight distinct splits to choose from, stemming from choosing either the entirety of tmQM wB97MV or just the neutral structures, and four splits for each.