With the classification mindset in mind, how many classes would one expect in the dataset constructed in this work (containing only eleven elements)? Using IR peak tables from reference 74, the authors of this text counted and found that the number was on the order of 150-200 unique frequencies associated with unique functional groups that could be distinguished in graph structure by Graphormer-IR. The variance in this number comes from the extent to what one considers to be a unique "graph structure": is the C=O stretching frequency in a 𝛽-lactone part of a meaningfully different class than C=O stretch of a 𝛾-lactone if they both occur ca. 1,750±25 cm -1 ? The important part of this exercise is that the number of attention heads is close to the number of possible unique frequency/graph structure pairs. It is also important to note that attention heads are a mathematical tool used to understand relationships in the input data that aren't strictly limited to structure to frequency. The number of frequencies is smaller than the total number of heads because there are likely attention heads attending to other relationships related to global graph structure or long-range interactions that aren't unique to a functional group like hydrogen bonding. We propose that the reason the model performs better with 210 attentions heads is because attention heads used in the state-of-the-art Graphormer-IR becomes a specialist in that frequency and is responsible for casting attention such that the model can successfully predict that peak alone. When adding together all the attention scores from each frequency specialist, the final prediction made is the sum of all the individual peaks developed by each attention head.