Infrared (IR) spectroscopy is crucial in various chemical and forensic domains, but faster in silico methods for predicting experimental spectra are needed due to the time and accuracy limitations of ab initio methods. We employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra using only Simplified Molecular-Input Line-Entry System (SMILES) strings. Our dataset includes 53,528 high-quality spectra with elements H, C, N, O, F, Si, S, P, Cl, Br, and I in five solvent phases. When using only atomic numbers for node encodings, Graphormer-IR achieved SIS_μ test scores of 0.8449±0.0012 (n=5), surpassing the state-of-the-art Chemprop-IR (SIS_μ = 0.8409 ± 0.0014, n=5), with only 36% of the encoded information. Augmenting node embeddings with additional node-level descriptors in learned embeddings generated through a multi-layer perceptron improves scores to SIS_μ = 0.8523±0.0006, a total improvement of 19.7σ. These improved scores show how Graphormer-IR excels in capturing long-range interactions like hydrogen bonding, anharmonic peak positions in experimental spectra, and stretching frequencies of uncommon functional groups. Scaling our architecture to 210 attention heads demonstrates specialist-like behavior for distinct IR frequencies that improves model performance. Our model utilizes novel architectures, including a global node for solvent phase encoding, learned node feature embeddings, and a 1D smoothing CNN. Graphormer-IR’s innovations underscore its potency over traditional message-passing neural networks (MPNNs) due to its expressive embeddings and ability to capture long-range intra-molecular relationships.