Expanding the dataset to include open-shell complexes would greatly increase the chemical information contained therein, as would additional charged data. This is supported by the fact that models trained on even a small amount of charged data demonstrate continued improvement of model performance with increasing dataset size, instead of leveling off, which occurs when only neutral structures are used. The combination of a much larger and more diverse dataset (in terms of both charge and spin and reactive intermediate structures) with a sufficiently discerning and performant model architecture could allow for accurate prediction of complex reaction pathways in homogeneous catalysis. There are two major directions for future work: 1) developing models capable of differentiating between and simultaneously learning energies of structures with different charges and spin states, and 2) building an expanded dataset that includes a much wider variety of charge and spin states to enable models trained on such data to be used in catalysis applications. These approaches should seek to demonstrate that, given a large and chemically diverse dataset, ML models can generalize across the homogeneous catalyst space.