After these mappings were created, ML models were used to predict energies for the entire dataset, and then the absolute residuals were used as the heatmap colors (the figures shown use a GemNet-T model trained on 80% of the respective dataset). These are shown in Figure for tmQM and Figure for tmQM wB97MV. In order to make the residuals more visible, the points were plotted by the absolute value of their residuals on the z-axis, and then projected back into two dimensions to ensure that the points with more error are on the top of the plot. Figure shows that the iron-group metals were predicted particularly poorly for a model trained on tmQM, while Figure shows that there are no metals that GemNet-T predicts particularly poorly compared to the rest when trained on tmQM wB97MV (note the substantially different scale). This lack of error clustering, as well as generally lower error, indicate that the models are more consistent and generalizable when trained on tmQM wB97MV compared to when trained on tmQM.