Graph Feature Encoding. In using Graphormer to predict infrared spectra, we explored the best way to encode atomic and edge descriptors into the graph structure. A detailed summary of these methods is available in reference . The emphasis of this work was on understanding how to balance a knowledgeable model initialization (by using descriptors like hybridization, Gasetgier partial charge, etc.) and providing a flexible description of the local chemical environment to not shoehorn model into an overly specific human-engineered description. We found that the optimal architecture involved using a learned feature encoder (3 in Figure ), where node features are projected into a fixed-size latent space using a small neural network. All hydrogens are described implicitly (as node features), with the exception for those that define stereocenters. We describe edge features using the combinatoric mapping scheme utilized in the Graphormer-IR study. All node and edge features used to initialize these encoders can be found in Tables and with associated domains.