Next, we turn to the PAH101 dataset. Parity plots for models fine-tuned with OCELOT from OMDB-trained baseline models with and without angle featurization are shown in Figure and Figure , respectively. As in Figure , the non-blue colored dots represent polymorphs within the PAH101 dataset. Both the R 2 and MAE values in Figure -b are fairly similar to Figure , similar to the behavior seen with ROY. The MAE decreases by 1.5 % from Figure to Figure and by 2.1 % from Figure to Figure . However, the MAE is lower and the R 2 value is higher for the polymorphs of the PAH101 dataset in comparison to Figure . Some sets of polymorphs experienced improved property prediction when finetuning was implemented in comparison to the baseline OCELOT-trained model while some experienced decreased performance. For example, CORONE (orange), and PERLEN (purple) experience an increase in MAE when fine-tuning is used regardless of graph representation. However, BIPHEN (yellow) and QQQCIG (red) experienced a decrease in MAE regardless of the graph representation. In some cases, fine-tuning improved performance only if angle featurization was used; KIDJUD (green) had a lower MAE compared to both OMDB-and OCELOT-trained models while BENZEN (magenta) had a lower MAE than the baseline OCELOT-trained model after finetuning, but still not as low as the OMDB-trained baseline model. On the other hand, some structures experienced improved performance when fine-tuning was used on the graph representation without angle featurization, such as GUQZUP (blueviolet) and DBPHEN (maroon). Performance for these two structures after fine-tuning with angle featurization was largely the same compared to both baseline models. However, in general, MAE decreased by 11.7 % from Figure to Figure for the polymorph sets, but actually increased by 12.6 % from Figure to Figure . Generally, the models that used angle featurization performed better for the polymorphs of QQQCIG, DBPHEN, BIPHEN, GUQZUP, KIDJUD, and BENZEN. Models without angle feturization performed best for the polymorphs of CORONE and PERLEN. Across all baseline and fine-tuned models, those without angle feautrization outperformed those with angle featurization in one case, which is PERLEN. PERLEN, or perylene, has two polymorphs within the dataset: beta and the E1-beta. They crystallize in the same space group, P 21/c, and adopt similar, herringbone packing motifs. The main difference appears to be on which symmetry element on which the asymmetric molecule is placed. Given that we expected angle featurization to improve the ability of the model to distinguish between crystal structures with similar packing motifs such as the polymorphs of PERLEN (packing motifs shown in Figure ), the performance here is surprising. These models do in fact perform better for other structures that have polymorphs that have similar packing motifs to each other, such as QQQCIG (packing motifs shown in Figure ) BENZEN, and BIPHEN (packing motifs shown in Figure ) so perhaps PERLEN is an outlier in its behavior. We would need to look at a larger dataset to glean any larger trends.