The proposed heterogeneous graph structure lends naturally to the use of GNNs for DL. In our heterogeneous GNN (Figure ), we first embed information from each node into a continuous vector space. We construct the dopant node embeddings by passing the dopant type (Z i ) through an embedding layer, then contextualizing the initial embedding on the dopant concentration (x i ) and the radii (r i0 and r if ) using featurewise linear modulation (FiLM) layers, as shown in Figure . Likewise, to obtain the embedding vector for the interaction nodes (Figure ), we pass the interaction type (τ ij ) through an embedding layer and then condition on the integrated interaction using a FiLM layer. A batch normalization is applied prior to the FiLM layer to shift the distribution of integrated interaction values. Note that "contextualizing" or "conditioning" a vector (i.e., an initial embedding) on another value (e.g. dopant concentration, layer radii, or integrated interaction) with a FiLM layer is a way of combining the information contained in each via an operation that is controlled by many learnable parameters, which often provides better expressivity and performance than a simple concatenation or addition. The resulting dopant and interaction embeddings are then used as the inputs for three iterations of message passing (MP) based on the heterogeneous graph's directional edges, where each MP iteration employs graph attention via the GATv2 operator, after which we use mean aggregation to obtain the global latent representation. Finally, a fully connected neural network (FCNN) is used for label prediction (i.e. predicting the log of the emission intensity over the specified wavelength band) from the global latent representation.