In this work, we performed an ablation study of the features utilized in the final full-features model on a single test split. The results from this study are available in Table . The 1D-CNN did not improve model performance in the case of learned feature embeddings (𝑆𝐼𝑆 𝜇 = 0.8503, see Table ). We found that inclusion of the 1D-CNN slowed down the rate of learning in early epochs, eventually resulting in overall lower model accuracy. The ablation study also showed that the inclusion of additional edge features by using the combinatoric method (discussed above) provided only minor improvements to model performance (𝑆𝐼𝑆 𝜇 = 0.8531) as compared to a model that only included bondtype features (𝑆𝐼𝑆 𝜇 = 0.8521). Including a global node had a large, positive impact to model performance; when removed, SIS exhibited a dramatic decrease to a value of 0.8109. Although our correction procedure does standardize spectra, it does not eliminate the entirety of the solvent baseline (as can be seen in Figures and). As such, the inclusion of a description of the phase in the global node allows models to learn the unique absorbances associated with each phase and increases model performance. The global node also likely improves predictions of bathochromic shifts, wherein the solvent/phase environment can cause changes in peak frequency. We also explored a scenario whereby all node features were eliminated by setting them to a uniform, constant value. Despite having no encoding for atomic number, Graphormer-IR still achieved a score of 𝑆𝐼𝑆 𝜇 = 0.7006. While this performance is much lower than all other models, it shows how Graphormer-IR learns from the graph structure and implicit bonding patterns such that it can still discern partial vibrational frequencies from each molecule.