This trend is especially pronounced on the learning curves for models trained on the neutral subset, with these curves almost completely leveling off and in some cases actually rising with the last data addition. The neutral only models do perform much better than those trained on all of tmQM wB97MV, which is expected since the models used do not incorporate charge information. However, despite their worse overall prediction, we note that the models trained on structures of multiple charges do exhibit continued decrease in MAE, and for both PaiNN and GemNet-T, the rate of that decrease remains nearly constant up to the full training set size. A model capable of differentiating between and simultaneously learning energies of structures with different charges would almost certainly improve predictions on tmQM wB97MV. Our observations indicate that the additional chemical information contained in data of multiple charges is critical for continued improvement of model performance with increasing dataset size. We further hypothesize that expanding the dataset to include open-shell complexes would also greatly increase the available chemical information. Therefore, the combination of a much larger and more diverse dataset (in terms of both charge and spin) with a sufficiently discerning and performant model may allow for accurate ML prediction of redox reactions that are common in homogeneous catalysis and models capable of predicting complex reaction pathways more generally.