The only other large-scale attempt to predict infrared spectra from molecular structure using deep learning has been performed in the work of McGill et al.. Due to the differences between the dataset used in McGill's work and in this work (constituent spectra, baseline correction, etc.), we chose to install the Chemprop-IR source code included in reference 37 and re-train using our dataset for comparison purposes. In McGill's study, DFT-computed infrared spectra were predicted using the same architecture as a test of transfer learning, and those spectra were made available in the published work. With advice from McGill, identical train/test splits of the computed IR spectra were generated for Chemprop-IR. Using these splits, MPNN models were trained, and the test scores reported in reference 37 were successfully reproduced, thus ensuring correct implementation of the Chemprop-IR code.