In this work we have explored how GNN transformers can be applied to the prediction of infrared spectra using only SMILES strings as input. IR spectra were obtained from a variety of different online sources that were measured on different instruments and in five unique phase environments (nujol mull, gas phase, KBr disk, CCl4, liquid film). Spectra were pruned to only high-quality spectra of molecules containing 11 elements (C, H, N, O, F, Si, P, S, Cl, Br, and I), then standardized using a novel baseline correction algorithm, exclusion of solvent absorbance peaks, interpolation, and padding to produce machine-learnable data. Molecular graphs consist of edge features (e.g., bond type, stereochemistry, etc.) and node features (e.g., atomic number, hybridization, partial charge, etc.). Phase was encoded through use of a global node that was connected to all other nodes by a custom edge type. Graphs were then passed to Graphormer, which generates a learned molecular fingerprint that is analyzed by a multi-layer perceptron to produce the final IR spectrum. Using only atomic number as a node feature, Graphormer-IR obtained final scores of 𝑆𝐼𝑆 𝜇 = 0.8449 ± 0.0012 (n=5). To validate these scores, the previous MPNN model, Chemprop-IR, was installed from reference 37 and tested using identical test, validation, and training splits. Chemprop-IR achieved test 𝑆𝐼𝑆 𝜇 = 0.8326 ± 0.0015 with only atomic number as a node encoding and 0.8405 ± 0.0013 with the full suite of node level features. This proved that with statistical significance (3.3𝜎), Graphormer-IR (atomic number only) is the state-of-art-model for prediction of infrared spectra.