As noted in the baseline correction section above, the multiplication factors of 𝑦 𝑝𝑟𝑒𝑑,𝑖 and 𝑦 𝑡𝑎𝑟𝑔𝑒𝑡,𝑖 in the loss function provide innate scaling such that incorrect prediction of large absorbances will be more severely punished than incorrect prediction of values near zero. While this effect is desirable when examining peaks, an unintended consequence is that for broad regions of moderate absorbance, like from solvent baselines, the sum of correct large number of moderate intensities will contribute meaningfully to the composite SID value. As such, without baseline correction, models would tend to learn solvent baselines, which would inflate the prediction scores of "messier" spectra. Spectral Information Similarity Score. To consistently evaluate the success of a model in measuring the extent of similarity between two spectra across a set of predictions, a consistent scalar metric is needed. Once again, we borrow a method known as Spectral Information Similarity (SIS) from the work of McGill to maximize the comparability of Graphormer-IR with respect to Chemprop-IR. 37 Test predictions in our model are first normalized such that the fingerprint region (600-1,500 cm -1 ) sums to unity. This procedure was performed in training in the Chemprop study, but we found that it decreased performance and interfered with model gradients. The goal of this normalization is to emphasize the weight of the fingerprint region in evaluation by increasing the relative intensity of peaks within this region. To reproduce this scaling effect, during evaluation of fully trained models, the fingerprint regions of all spectra are first normalized to sum to unity. Then, both predicted and target spectra are smoothed with a gaussian convolution with a standard deviation of 15 cm , which is comparable to typical peak-to-peak distances in IR spectra in our dataset. The goal of the gaussian convolution is to provide some tolerance with respect to minor deviations or noise in both the model predictions and the ground truth spectra themselves. While this step is not necessarily needed due to the inclusion of the 1D-CNN, it was done to ensure internal consistency between Graphormer-IR and Chemprop-IR. Spectra were then normalized to globally sum to unity and SID scores are calculated using equation (2). In both models, SID was calculated using wavenumber values from 500 cm -1 to 4,000 cm -1 , since the lowest possible vibrational frequency in the dataset, C-I stretching, is known to occur between 500-600 cm -1 . SID is then converted to SIS by rescaling the score to a range of 0 to 1, as shown in equation 5: