When comparing the best performing Graphormer-IR model (𝑆𝐼𝑆 𝜇 = 0.8523±0.0006) and the best performing Chemprop-IR model (𝑆𝐼𝑆 𝜇 = 0.8405±0.0013), we find a roughly 20𝜎 improvement in terms of SIS score. Figure shows several examples highlighting instances in which Graphormer-IR meaningfully improves the predictions made from Chemprop-IR on identical test splits after training on identical training/validation splits. It is important to acknowledge that Graphormer-IR does not universally outperform Chemprop-IR on all spectral predictions. The goal of figure 7 is to illustrate instance in which the novel architecture of Graphormer-IR can provide predictions of emergent chemistry that Chemprop-IR/MPNNs are unable to describe. Notable improvements to spectral features are highlighted in gray. In Figure , the predicted gas-phase spectra of (isocyanatomethanetriyl)tribenzene are displayed alongside the ground truth (black) and demonstrated an impressive Δ𝑆𝐼𝑆 𝜇 = +0.42 for Graphormer-IR predictions (red) as compared to Chemprop-IR predictions (green). This large improvement is largely in part due to the successful prediction of the N=C=O stretching frequency at 2,250 cm -1 . While Chemprop-IR does predict a minor peak at 2,000, it fails to capture the real peak intensity and location. Graphormer-IR can capture this frequency and reasonably approximate the intensity profile, suggesting that the embeddings generated from Graphormer can more effectively describe three body stretching frequencies than those generated by MPNN frameworks.