For large models, a traditional deep learning approach involves providing models the general information needed to understand the problem of interest rather than providing human-understood heuristics and quantities, thus allowing the model to infer relevant characteristics from context, relative node importance, and data structure. In doing so, models can develop learned representations without being bound to the same human-understood descriptors, allowing for more flexible machine understanding and improved expressiveness. Atomic number is the prototypical "minimal" descriptor for molecular graphs because it provides sufficient information for a model to infer other characteristics when considered in the context of graph structure and edge encodings. This is best exemplified by hybridization, a commonly used node encoding in graph neural network descriptors that could prove useful in the prediction of infrared spectroscopy. An atom's hybridization can give vital insight into bonding angles and distances, which are known to directly correlate with the energy of a molecular fragment's bending and stretching frequencies. But could a description of hybridization prove redundant by a sufficiently expressive neural network? For instance, a carbon node that is connected to 4 other nodes in a graph will always be sp 3 hybridized and will have corresponding bonding angles and lengths. By explicitly describing atomic hybridization, the node feature may unnecessarily shoehorn the model into a description that is not flexible enough for the complexities of "real" molecular environments. In the set of all possible sp 3 carbon environments, the constituent bonding partners could dramatically change the bond lengths and angles. This variance may be better suited to a learned understanding of hybridization, wherein a model can derive its own understanding of the bonding environment of the 2D chemical graph.