To assess the performance of the heterogeneous GNN ("hetero-GNN") described above with respect to other models and representations, we train each on the SUNSET-1 dataset, where our target label is emission intensity from the UV-blue (300-450 nm) wavelength band, and training data include 800-nm excited UCNPs with 0-3 shells. We evaluate the mean squared errors (MSEs) of each model when predicting the intensities of held-out ID samples as well as for OOD nanoparticles with 4 shells (Table ). We compare the hetero-GNN to four well-established supervised learning models: two models (a random forest regressor and a FCNN multi-layer perceptron) using a tabular representation; a CNN using an image representation; and the homogeneous GNN described above. Model hyperparameters are provided in Section S8. Additional model, representation, and feature details are provided in Sections S4, S5, and S6.