In essence, this study makes significant progress in precisely predicting infrared spectra through deep learning approaches. Our discoveries underscore that transformer graph neural networks achieve cutting-edge performance. We've highlighted numerous valuable improvements in model architecture and innovative training techniques, including the integration of global feature nodes, specialized attention heads, and learned node feature embeddings. These advancements have notably enhanced the impromptu forecasting of complex chemical interactions in infrared spectroscopy, providing an attractive alternative to time inefficient ab initio methods. While a step forward, there still exists large room for improvement to model predictions and other areas to explore in prediction of IR spectra. Emergent architectures like GPS++ which amalgamate the local focus of MPNNs with the global attention of transformers, have already exhibited enhanced performance compared to pure graph transformers. Challenges remain in precisely predicting outcomes for intensity profiles and atypical functional groups. Furthermore, the potential application of graph transformers to related IR techniques like Near-IR (NIR) spectroscopy, Raman spectroscopy, or Infrared Multi-Photon Dissociation (IRMPD) remains underexplored. In those regards, our work is ongoing.