Here, we investigate the implementation of Graphormer 46 to improve the prediction of infrared spectra using deep learning. Graphormer, a Microsoft-developed graph neural network that adapts the transformer architecture, was the winner of the 2021 Open Catalyst challenge. By utilizing a global receptive field (i.e., all nodes in the graph exchange information with all other nodes) and the attention mechanism that allows for contextual weighting, Graphormer has achieved state of the art predictions in molecular HOMO-LUMO gaps, inhibition of HIV viral reproduction, biological activities, and molecular docking. At the time of writing, Graphormer has only been applied to spectroscopic predictions to tandem mass spectra (MS 2 ). In this work, we show how Graphormer can be used to improve on the groundwork laid by McGill, demonstrating the immense expressiveness achieved by graph transformer network in the prediction of IR spectra.