Our second test case is the PAH101 dataset, which contains 101 organic molecular crystals extracted from the CSD. We have augmented the dataset with some additional polymorphs where available from the CSD. All crystal structures were optimized using the settings described in Section 3.3. The distribution of the bandgaps for this dataset is shown in Figure , with an average bandgap of 2.143 eV. Models trained on the OCELOT database with and without angle featurization are shown in Figure and Figure , respectively. Models trained on the OMDB with and without angle featurization are shown in Figure and Figure , respectively. Similar to Figure , angle featurization appears to significanly improve the performance of the models on both the general dataset and the polymorph sets within PAH101. The MAE decreased by 38.0 % from Figure to Figure and by 35.0 % from Figure to Figure . For polymorphs, similar gains in performance were seen with a MAE decrease of 34.9 % from Figure to Figure and of 34.0 % from Figure to Figure . Figure has the highest R 2 value and the lowest MAE of 0.724 and 0.324, respectively. On the other hand, Figure has the lowest R 2 value and the highest MAE of 0.142 and 0.655, respectively. Figure has a higher MAE but also a higher R 2 value than Figure . This could be because the data examined here is closer to the average bandgap in the OCELOT database, which is 2.31 eV as shown in Figure . Figure clearly shows the most correlation between the structure and the predicted bandgap, particularly around the 1-3 eV bandgap region. The models trained on the OMDB perform worse on the PAH101 dataset in comparison to the polymorphs of ROY. In the low bandgap region, below 2 eV, the results appear to be independent of the graph representation, i.e. angle featurization does not seem to significantly improve results. In the higher bandgap region, however, angle featurization does seem to improve the correlation between the structure and the predicted bandgap. There is some overlap between the OCELOT database and the PAH101 dataset, given in CSD reference codes: CORONE01, BIPHEN, DBZCOR, KIDJUD03, GUQZUP, DPANTR08, KAGGEG, and ANTCEN, or a total of 8 out of 107 structures. Despite this, these results imply that including polymorphs in the training data, such as OCELOT, can actually lead to more generalizable models; the models trained on this dataset outperform those trained on the OMDB.