Green hydrogen offers a promising alternative to fossil fuels, since it is clean, energy-dense, and does not produce any greenhouse gases when produced or when burned. However, green hydrogen's economic viability depends on the discovery of cheaper and more efficient catalysts. A large challenge in discovering these novel catalysts lies in the enormous chemical space of potential candidates. Conventional experimental methods require the synthesis, characterization, and assessment of new structures, which is a very time-intensive process that is neither able to completely cover the vast chemical space nor do so in a reasonable amount of time. To overcome this challenge, computational approaches have been employed since certain material properties, like electronic energy, can be correlated with catalytic activity. Methods such as density functional theory (DFT) can be used to screen out catalysts that likely have poor performance without the time investment of experimentation. While useful, a single DFT simulation can still take on the order of days for systems of moderate complexity, precluding comprehensive high-throughput analysis. trained on these large datasets, especially graph neural networks (GNNs), show promise for many applications. Despite the fact that homogeneous catalysts offer tremendous opportunities for selectivity and tunability that heterogeneous catalysts do not, the field has been lacking similar large-scale, diverse datasets for homogeneous catalysis until the tmQM dataset was published in 2020. Additionally, to the best of our knowledge, no ML potential specifically trained on tmQM has been unveiled; Balcells et al. have since released a modified version of the dataset -tmQMg -which was used to train various graph models.