Qualitatively, it was also observed in model training that noisier data (e.g., a higher solvent baseline) would often yield higher accuracy/lower loss values. This is because models learned to propagate noisy baselines into predictions because the loss function would reward higher intensity values (seen in the 2,000-4,000 cm -1 range in figure ). While the intended effect of the loss function is to reward correctly predicted high-intensity peaks, if there are enough noisy spectra in the dataset, the model will instead learn to reward baseline noise. This may have been a factor in the MPNN study (no baseline correction), where predictions of condensed phase spectra that had higher average baseline noise performed were better on average than gas phase spectra. To account for this loss function bias, spectral baseline correction was applied universally to correct for different experimental conditions and baseline noise. This correction was performed using the improved asymmetric least squares method described by He et al., and implemented in Python in the work of Haack et al. with baseline parameters λ = 3.0x10 5 , λ 1 = 1.0x10 -3 and p = 0.04.While originally designed for Raman spectroscopy, our baseline correction technique minimizes the overfitting that traditional asymmetric least squares fitting suffers from by only considering the smoothness constraint at the second derivative. This technique is equally as suited for IR spectroscopy as Raman spectrocopy because of the rising baseline noise suffered by both methods. The KBr disk IR spectrum of 2-((pyridin-2ylamino)methylene)malononitrile in figure has a large rising baseline from 2,000-4,000 cm -1 likely arising from water absorption in the salt disk; this is corrected appropriately by the algorithm. In contrast, figure shows a spectrum with minimal baseline noise, which is relatively unaffected by the correction procedure.