The ability of Graphormer-IRIS to accurately predict the (often dissimilar) spectra associated with different charge states of the same molecule arises from the richness of the description generated by the global charge node. For example, Graphormer-IRIS captures subtle effects such as shifting band positions due to hydrogen bonding interactions and red-shifting of carbonyl frequencies upon sodium adduction. The use of UMAP visualization provides additional explainability, demonstrating that the initial embeddings of the Graphormer-IR model provide a rich description of chemical structures, leading to an emergent understanding of functional groups and generalized trends in how electron density impacts IR frequencies. These rich embeddings provide the foundation of understanding for the successful predictions made by Graphormer-IRIS via transfer learning. Similar dimensionality reduction of the final Graphormer-IRIS models shows that its embeddings retain the knowledge of functional groups from Graphormer-IR, and that the charge encoding communicates a contextual understanding of likely sites of charge state derived from the global charge node. Given the richness of the "learned" chemical intuition that we observe in our model, we speculate that experimental IR(IS) data are an avenue to train "generalist" foundational deep learning frameworks by describing the patterns in molecular structure and bonding that are not obvious from a simple molecule graph. To employ IRIS as a tool for annotation of unknown features detected by mass spectrometry or for self-driving process optimization, it is necessary to overcome the current issues of data scarcity and the complex nuances of molecular charge. Here, we achieved this using multi-staged transfer learning to pass a learned "chemical intuition" from one model to another. Although a meaningful step forward, there remains a great deal to accomplish en route to improved MS feature annotation. For example, Graphormer-IRIS can provide accurate spectral predictions for small molecule metabolites, but our experimental training set requires significant expansion to create a generally applicable model. Such a model could be used as a discriminator for nontargeted MS feature annotation. ML-predicted IR and IRIS spectra could also prove valuable in a closed-loop selfdriving framework for process optimization.