Using five identical training, validation and test splits each consisting of only unique SMILES strings, Chemprop-IR and Graphormer-IR models were trained and tested. As discussed above, models were trained under two separate conditions for comparison purposes: (1) using only atomic number as a node-level feature, and (2) using the full suite of node features. From this point, unless otherwise mentioned, all models utilized the full suite of edge features discussed above. Test score distributions for the best performing Graphormer-IR split are shown in figure . Figure shows that half of the predicted spectra have an SIS greater than 0.87, indicating accurate prediction of most major and minor peaks, with some minor discrepancies in band intensities. The long tail of the distribution is associated with lower quality spectra and unrepresented functional groups, which lead to poorly learned spectral features. Figure presents example spectral predictions, showcasing representative spectra from each percentile range (10th to 100th) featuring a variety of chemical structures and sizes. Generally, Graphormer-IR exhibits optimal performance for molecules containing large carbon chains and for those containing functional groups with conjugated systems, oxygen (e.g., ethers, esters, carboxylic acids, alcohols), and nitrogen (amines, amides, nitriles). This enhanced performance is particularly evident in the spectra from the 60th to 100th percentiles. Graphormer-IR was successful in predicting spectra for some molecules that contain rare functional groups (with respect to the dataset). For example, figure shows the spectrum of (Z)-1-(4-(2-methylbutyl)phenyl)-2-(4-(pentyloxy)phenyl)diazene 1-oxide, which is near the high end of the SIS distribution (100 th percentile). Band positions and peak intensities are accurately reproduced, despite the molecule containing a highly delocalized electron density distribution and an uncommon (with respect to the dataset) 1,3 dipole associated with the O=N=N moiety. In figure (the 90 th percentile) the N=C=S stretching frequencies (ca. 2,050 cm -1 ) that are unique to the relatively uncommon isothiocyanate functional group are successfully predicted with high accuracy. Towards the lower end of the SIS distribution subtle molecular interactions are missed and intensity profiles are not correctly predicted for spectra with poorly represented molecular features like highly halogenated species (figure ), bicyclic rings (figure ), and symmetrically substituted cycloheptatriene (figure ). Figure provides an illustrative example in the spectrum of 1,2-dibromo-1-chloro-1,2,2-trifluoroethane, where halogenation at adjacent carbons creates subtly different chemical environments. This manifests in the ground truth spectrum as a pair of C-Br peaks (ca. 650-700 cm -1 ) that are offset from one other due to the asymmetry of the chlorine and fluorine substitution at those moieties. Rather than two bands associated with C-Br stretching motions, a single band with approximately twice the intensity is predicted, which significantly impacts the SIS score. The low SIS is likely worsened underrepresentation of halogenated compounds in the dataset (see figure ). Figure also illustrates a systematic(highly symmetric systems, small molecules) typically have lower accuracies than larger molecules. When calculating the SID loss, each point is scaled by its spectral intensity (see Equation ), meaning baseline values with lower intensity have a lesser impact on the SIS/SID value. As such, a spectrum with a small number of significant peaks will be more adversely affected by incorrect predictions of even a single peak or region. The gas phase SIS distribution (see Figure ) illustrates this in its long tail, where low SIS values are more common because small molecules are more readily measured in the gas phase. In addition to being smaller on average, molecules in the gas phase usually effect where spectra with fewer vibrational frequencies have lower polarity (and consequently lower changing dipole moments), resulting in weaker and fewer IR-active bands than Each spectrum is depicted with its calculated SIS, phase environment, and percentile location in the total test split (i.e., percentile 50 indicates that the spectrum is better than the bottom 40% of spectra, but worse than the top 50%).