From the results shown in Figures and, as well as Table , we see that, in general, GemNet-T performs better than both PaiNN and SpinConv, which perform better than SchNet, for both the models trained on the entirety of tmQM wB97MV and those using only the neutral structures. This broadly correlates with model complexity, age, and how well the models perform on other datasets, such as OC20. All models tended to perform better when the neutral only subset of tmQM wB97MV was used, likely because the lack of charge information in these models made it difficult to predict the energies of charged In order to visualize the dataset, structures were characterized using SOAP parameters, which give a quantitative way to determine the similarity between structures. Then, these parameters were condensed into two dimensions by using a tSNE map (after dimensionality reduction with PCA), creating a plot where structures closer to one another were more similar in chemical composition. These studies were done using a combination of the ASAP 36 and chemiscope 37 software packages. A tSNE map for tmQM is shown in Figure , and one for tmQM wB97MV is shown in Figure . The color represents the group of the metal center.