Chemprop utilizes a variety of additional node features to improve learning. All of these encodings were included in our work, except for atom chirality. A complete list of node/edge features including descriptions and possible domains is listed in Table . Atom chirality is not included because of previous work into deep learning using transformer/MPNN hybrids that found that a stereochemical description decreased model performance in predictions of HOMO-LUMO gaps. There is no meaningful reason that enantiomeric chirality will influence the vibrational frequencies of a molecule in enough cases to be represented in the dataset such that it could have a meaningful impact on model performance. Enantiomers are expected to have functionally identical IR spectra, whereas diastereomers are expected to have slight differences in peak positions. In all molecular graphs, hydrogens are considered implicitly to reduce the average graph size because transformers are known to perform better on smaller input due to hardware limitations. As discussed previously, the simple "atomic number only" approach for node encodings was first tested to determine the extent to which our models could discern infrared spectra with the minimum information possible, using only learned chemical relationships. To incorporate additional node-level features into Graphormer's encodings, two different methods were tested. For the first method tested, all integer features (i.e., all features except atomic mass and partial charge) were mapped combinatorically to integer classes representing each unique combination of all possible node features, analogous to the method used for edge features. This method provides a naïve approach to describing node features, wherein all possible node environments as defined by the feature set are unique classes that the model can use to distinguish vibrational frequencies. The second method tested employed learned feature embeddings, whereby node features are passed through an MLP in a variety of different combinations to obtain flexible initial node descriptions. Phase Encoding. The Chemprop architecture seeks to overcome the locality limitations of D-MPNNs via the introduction of normalized global features. After the learned molecular fingerprint is generated, global molecular properties are appended and then processed by the MLP amid spectral prediction. In doing so, the model can consider the total molecular environment instead of just the sum of local descriptions generated by the MLP. Yang demonstrated that this approach measurably improved model performance across a variety of regression and classification tasks. The most notable global feature used in the Chemprop-IR study was a description of the phase, with a one-hot encoding describing whether an IR spectrum was measured in CCl4, the gas phase, nujol mull, liquid film, or KBr disks. With this inclusion, the MLP can theoretically learn the approximate baseline profile of each phase and adjust spectral predictions accordingly.