All models were trained for 250 epochs, with a batch size of 32 and an 80-10-10 (%) training-validation-test split. Some molecules had multiple spectra measured in different solvent environments, but since these spectra could be expressed as unique graphs, all measurements were included. These repeat measurements were restricted to the same split (i.e., test, training, or validation) to mitigate possible data leakage between the training and test sets. All models were trained on a single NVIDIA GeForce RTX 3080 graphics card with 10 GB of video random access memory (VRAM). Training a typical model would take roughly 10.2 hours to complete. One of the major benefits of the machine learning approach to infrared spectral prediction is that once a model is trained, calculation of infrared spectra is extremely rapid. A typical test split evaluation using a trained model consisting of 5338 spectra took approximately 2.8 minutes, which is approximately 31 ms per spectrummuch shorter than the typical experimental measurement time.