For spectral phase, we expressed the encodings as distinct classes analogous to a "new" atom type label that the model could appropriate to describe the changes in behavior. By mixing this atom type with a null description for all other node descriptors, we provide a sufficiently descriptive encoding for the global phase that allows the model to learn the different spectral encodings. However, for the prediction of RTs, there are many useful empirical descriptions that would be lost by consolidating methods into a single class. Additionally, these empirical chromatographic parameters describe fundamentally different chemical phenomena and as compared to those for the descriptions of atom (node) environments. As such, mixing these parameter spaces in a single node feature encoder will almost certainly impinge on model learning.