It was also determined that by using many attention heads (210), each head becomes a "specialist" for each different frequency that could be produced from a chemical structure. By allowing each attention head to focus on only one relationship (and without the discourse of talking heads attention), the sum performance of the model with many highly specialized actors outperforms the approach with fewer heads that try to broadly encode more different types logic. This approach highlights a new possibility for how to use transformer models in regression problems that are built on coarse-grained classification problems. To the best of the author's knowledge, there has been no other application of large-scale implementation of attention heads to regression problems that involves coarse grained classification tasks.