On this note, we turn to the performance of the fine-tuned models on the test cases from the sections above. First, we examine the polymorphs of ROY, shown in Figure . Fine-tuning with and without angle featurization is shown in Figure and Figure , respectively. As can be seen, similar to Figure , there is little correlation between the predicted bandgap and the polymorph of ROY. However, the performance was significantly improved with an MAE of 0.184 eV rather than 1.497 eV. This is an 87.7 % decrease in MAE. The performance of Figure shows slightly less but still highly impressive results with a decrease of 77.0 % in MAE from Figure to Figure . However, the fine-tuned model of Figure still appears to struggle with the low bandgap region. For values closer to the average bandgap of 2.31 eV in the OCELOT database, fine-tuning appeared to significantly improve performance. We did attempt to improve the performance of the model in the low bandgap region through data augmentation. We applied random flip and random rotate transformations with a probability of 0.5 to data points with a bandgap of less than 2 eV. However, this led to almost identical performance, as seen in Figures S1 in the SI. Perhaps improvement could be made by adding different structures, not just transformations, to the existing dataset.