The most striking benefit of the hetero-GNN is its ability to extrapolate, in this case to 4-shelled nanostructures not included in its training set. When switching from ID to OOD testing, the MSE for the hetero-GNN model increased by 8.3 cps, or 1.6-fold. This modest increase in loss is in stark contrast to the tabular-representation-based RFR and FCNN models, for which extrapolation resulted in 6-to 10-fold increases in the MSE, respectively. The tabular models lack the geometric and relational information of the graph models and therefore must learn the influence of the dopants in each layer independently. This increases the data demand of these models, making them prone to overfitting and reducing their ability to predict the properties of unseen heterostructures. Even the image-based CNN, which had high ID accuracy, exhibited 2.8-fold greater loss during OOD testing. We ascribe the significantly greater ability of the hetero-GNN to generalize to its graph representation. This conclusion is supported by the fact that the homogeneous GNN also exhibits very little change in loss (+6.5%) when shifting from interpolation to extrapolation. In summary, representing UCNP heterostructures as heterogeneous graphs results in a hetero-GNN that exhibits both high accuracy and extrapolative capacity, in contrast to existing models that are inaccurate (homo-GNN), poor at extrapolation (image CNN), or both (tabular models).