To train ML models on the SUNSET data, we initially investigated several existing representations for encoding the compositional and dimensional features of each UCNP. As we summarize in Table and discuss in more detail in the "Model Performance" section, we found that standard ML models (e.g., random forest regressors and convolutional neural networks) utilizing tabular and image-based representations exhibited poor ability to extrapolate, with 3-to 10-fold lower accuracy during out-of-distribution testing (OOD) than during in-distribution testing (ID).