Our first test case is the highly polymorphic 5-methyl-2-[(2-nitrophenyl)amino]-3thiophenecarbonitrile (ROY). We choose ROY because it is highly polymorphic and allows us to compare to previous work. ROY has 11 polymorphs that are available in the CSD, which were extracted and then optimized with the settings described in Section 3.3. Parity plots for the 11 polymorphs are shown in Figure . Figure shows models trained on the OCELOT database with(a) and without (b) angle featurization and Figure shows models trained on the OMDB with (c) and without (d) angle featurization. As can be seen, angle featurization significantly improves the ability of the model to predict the bandgap of the polymorphs of ROY. The MAE decreases by 54.9 % from Figure to Figure from by 68.4 % from Figure to Figure . Figure has the highest R 2 value and the lowest MAE of 0.594 and 0.109 eV, respectively, while Figure has the lowest R 2 value and highest MAE of -8.609 and 0.491 eV, respecitvely. Figure /c have lower MAE than our previous work, which had a MAE of 0.40 eV on the polymorphs of ROY. For Figures there is little correlation between the polymorph and the predicted bandgap. This is fairly consistent with our previous work. However, there is fairly strong correlation between the polymorph and the predicted bandgap in Figures . This demonstrates that incorporating angle featurization is crucial to describing polymorphic materials in graph neural networks. The models trained on the OMDB outperform the models trained on the OCELOT database. Similar to Figure , this could be due to the fact that the OMDB has more materials in the bandgap region of the polymorphs of ROY. As seen in Figure , the performance of the model does improve as the bandgap of the polymorph approaches 2 eV.