In general, a limitation of both Chemprop-IR and Graphormer-IR predictions was that models were not able to consistently predict peak intensity profiles. This shortcoming is prominent in the lower percentile spectrum displayed in figure , where peak positions are often correct, but predicted intensities are often much lower or higher than the ground truths. McGill posited that this effect was due in part due to unknown parameters in measured spectra like analyte and solvent concentration that make relative intensities uncertain. To attempt to improve predictions of intensity profiles, partial charge was included as an additional node feature in. Local electron density plays an important role in the relative intensity of peaks in IR spectra, so having even an approximate value could theoretically help models better judge peak intensities. Since partial charge is a float feature, it cannot be readily mapped as a one-hot class like integer features. In this work, we tested adding the pure partial charge to atomic number, as well as combining the atomic number encoding with a learned representation of partial charge to produce the final node embeddings. The latter of these options was found to perform better, but only produced an 𝑆𝐼𝑆 𝜇 of 0.8399. This performance is worse than the pure atomistic encoding, suggesting that whatever derived understanding of partial charge learned by the model is better for predicting peak profiles and positions. This is likely due to the way in which partial charges were calculated, using the method of Gastegier , which is known to perform well for organic molecules containing C, H, N, and O. However, there are a variety of assumptions containing in these calculations (e.g., assumed partial charge "starting" values) that do not lend themselves to understanding systems with unconventional dipoles (e.g., molecules with thiocyanate, ketene, or nitroso groups) or highly delocalized charges. The decrease in performance associated with the inclusion of partial charges is likely because of these approximations, where Gastegier partial charge does not do a good job at completely describing unconventional electron densities, shoehorning model predictions into poorer predictions of intensities. Table . Model performance (n=1) as a function of different node encoding schemes. Complete descriptions of the node encodings can be found in Table . The second encoding method involved mapping all integer node features combinatorically into numeric class values as discussed in the Methods section. It was observed that the combinatoric mapping of node features (𝑆𝐼𝑆 𝜇 = 0.8440) performed worse than the state-of-the-art model (𝑆𝐼𝑆 𝜇 = 0.8531), as shown in table 2. To understand why this model was limited in scope, one must consider the representation of this embedding space. Naïvely, the possible encoding space as described by the chemical restrictions in the Methods section (e.g., only eleven possible elements) would suggest that the total possible number of atomic classes using this method would be 194,040 (the product of the number of classes per features). When this mapping was performed on the 53,528 relatively diverse (See figure ), molecules in our dataset, only 98 of these classes were represented, with the largest plurality being associated with carbon environments. This fact reveals two things about this dataset and chemical feature engineering more broadly. First, something that chemists have been claiming for centuries: molecules bond predictably. The second and more critical insight is that the set of commonly used integer descriptors for deep learning with GNNs contains a great deal of redundancy. A complete description of all the possible one-hot encodings for a given atom is too specific and rigid of a description for model learning. It is likely that within those 98 classes there is meaningful overlap between IR frequencies produced from each node. For example, single C-H bonds associated with both sp 2 and sp 3 carbon atoms exhibit very similar vibrational frequencies. By defining these cases as different classes, the model will learn to discriminate between these two classes in the output and may not reconcile the common frequencies generated by these contexts. In other words, each node is over-described by providing a hard classification that doesn't necessarily encode all the subtleties of experimental IR bands.