Of the five models tested, the hetero-GNN exhibited the lowest error for both ID and OOD testing, with MSE values of 13.9 and 22.2 photon counts-per-second (cps), respectively (Table ). The ID loss is 4-fold lower than that of models utilizing tabular representations (RFR and FCNN) and 21.5% lower than the CNN utilizing an image representation. The fact that the image-and heterogenous graph-based models have the best ID accuracies demonstrates how their representations allow them to leverage spatial information to learn relationships between heterostructure and properties and to connect the common behavior of dopant ions of the same type but located in different regions. It is notable, however, that the least accurate model for ID testing was also based on a graph representation. The homogeneous GNN exhibited 6-fold higher MSE than its hetereogeneous analogue, highlighting that the enhanced accuracy of the hetero-GNN is the result of its incorporation of interactions as nodes in the graphs.