Analysis of these test results demonstrated a variety of ways in which Graphormer-IR outperformed MPNN frameworks. First, the baseline-corrected data allowed Graphormer-IR to obtain scores where the inclusion of solvent data did not inflate results, and that the gas-phase IR spectra provided the most representative test scores as compared to the MPNN study where phase effects resulted in inflation of test scores. Comparison of test predictions showed the Graphormer-IR could more accurately discern anharmonic peak locations, predict uncommon frequencies from more than two atoms (i.e., N=C=O, N=C=S, stretching), and prediction of hydrogen-bonding interactions than the MPNN model could not parse. Ablation studies showed that combinatoric edge encodings and the global node provided meaningful benefits to predictions, whereas the 1D-CNN and talking heads attention did not. At the time of writing, the authors of this text believe that this a use global node to encode global graph features to improve learning is novel. By improving this information at the graph level, the graph encoder can apply the global information with context and relative importance in a way that was not possible in previous approaches and could theoretically be applied to any global information.