Absorbances arising from the solvent have well defined infrared frequencies. Spectra measured in CCl4 have large absorbances from 696-850 cm -1 and 1,500-1,600 cm -1 . Spectra measured in nujol mull contain broad absorbance from 2,750 to 3,000 cm -1 . While these peaks are consistent and theoretically learnable by a machine learning model, the relative concentration of the solvent/solute is not known across all spectra, leading to inconsistency in relative solvent peak sizes and intensities. As such, solvent absorbance frequencies were replaced with NaNs and excluded from normalization steps and accuracy measurements. All spectra were corrected using a baseline correction procedure (discussed in more detail below). Then, all spectra were normalized to sum to unity. While the goal of this baseline correction procedure is to standardize spectra and reduce variance due to baseline noise, it does not eliminate solvent effects from spectra. As such, a description of solvent is still needed for model learning, which is implemented as a global phase node. The implementation of this method is discussed in the Feature Engineering section below. Model Architecture.