In Figure , the spectral predictions for 4,4'-(butane-1,1diyl)bis(2,6-di-tert-butylphenol) are depicted showing an improvement of Δ𝑆𝐼𝑆 𝜇 = +0.25, largely explained by Chemprop-IR missing the broad absorbance from 3,200-3,500 cm -1 . In IR spectra, O-H stretching frequencies manifest as sharp peaks ca. 3,500-3,700 cm -1 . When alcohols experience inter-or intra-molecular hydrogen bonding (from the presence of solvents containing -OH or from high analyte concentrations), this peak becomes a broad absorbance from 3,200-3,500 cm -1 .4,4'-(butane-1,1-diyl)bis(2,6-di-tert-butylphenol) has two hydroxy groups and the -OH stretching frequency is broad and red-shifted due to intermolecular hydrogen bonding (intramolecular hydrogen bonding is also possible but unlikely due to the bulky tert-butyl groups adjacent to the hydroxides). Graphormer-IR can discern this interaction in a way that Chemprop-IR cannot because of its global receptive field. Because the embedding from each node considers all other nodes in the graph, the embedding generated from each hydroxide will encode information from each other and will "know" that there is more than one hydroxide groups in the molecule and that hydrogen bonding is possible. Conversely, the D-MPNN in Chemprop-IR uses a depth of 6, meaning that node-level information can only be propagated a maximum of 6 node hops away from the initial node. For MPNNs, embeddings for each of the hydroxide groups in the molecule will not contain information from the other group (see Figure ), leading to a non-prediction of broad hydrogen-bonding absorbance in the -OH stretching frequency. This difference in prediction accuracy exemplifies the richness of the embeddings generated by Graphormer-IR and highlights how architecture novelties allow GNN transformer hybrids to discern much more subtle chemical interactions than MPNNs. Molecules in Figure and 7D show two more examples of how Graphormer-IR can better describe disulphide linkages (7C) and polysiloxane bridges (7D) than MPNNs. These performance improvements illustrate how the rich descriptions generated by Graphormer/transformers are better when studying more uncommon functional groups and may provide additional utility when to fields like protein structure or polymer science where these groups are important.