In their study, McGill et al. showed that the spectral information divergence (SID) outperformed other loss functions in the prediction of IR spectra including mean squared error (MSE), root mean squared error (RMSE), and targetweighted mean squared error (TMSE). As such, we choose to implement this loss function into the Graphormer-IR framework as well. SID is a logarithmic measure that was develop by Chang as a spectral divergence measure and is defined as: where 𝑦 𝑝𝑟𝑒𝑑 and 𝑦 𝑡𝑎𝑟𝑔𝑒𝑡 are tensors containing intensities in the predicted and target spectra after normalization. Since logarithms require that all values be greater than zero, all spectra in our model are clipped such that the minimum intensity value in a spectrum is 10 -8 . Prior to calculating the loss, model predictions are normalized to sum to unity, and all NaN values are excluded from the calculation of the loss. The SID metric is defined such that the sum of the absorbances must be normalized to unity in each individual spectrum. In training, the SID value is scaled by a factor of 1,000 to help manage gradient overflow in the first few training epochs.