To support this claim with empirical evidence, we developed a subset of our complete dataset in which we only include molecules containing the elements C, H, N, and O. An 80-10-10 train-validation-test split was performed randomly on this subset of the data, once again obeying the restriction that spectra with the same SMILES string can be in the same split. This subset contained 34,382 spectra representing 19,557 unique SMILES strings. Model performance was studied as a function of the number of attention heads, keeping all other hyperparameters constant. Graphormer's embedding dimension must be divisible by the number of attention heads, so slight modifications to this value was made to accommodate different numbers of heads. The results of this study are depicted in Table : Table . Graphormer-IR performance (n=1) as a function of the number of attention heads. All models here are variations of the state-of-the-art model with learned feature embeddings. Results depicted in the "Only C,N,H,O" column were trained on a subset of the dataset that contained only those four elements. Note that embedding dimension must be divisible by the number of attention heads. , but to the best of our knowledge, there has never been a wide scale example of use of specialist attention heads for classification like in this work. The lower performance of the TH attention mechanism likely further supports the specialization hypothesis. For the same reason that a kitchen works better with highly specialized cooks that won't get overwhelmed by sharing tasks for which they are not experts, each attention head is best suited to parsing siloed relationships. In TH attention, the experts "talk" to each other at intermediate stages and may distract one another from performing their specialized task. An expert on fish won't have a lot of useful advice for the pastry chef; an expert in N=N=N stretching won't be useful to an attention head trying to understand C-Cl bending frequencies. By having a variety of specialists working individually on spectral prediction in a multi-headed attention mechanism, the sum of the highly specialized set of opinions performs better than one in which all work together at intermediate stages.