IRIS has been employed successfully to characterize numerous interesting chemical systems, and researchers continue to actively advance its application in new areas. For example, experimental and computed IR spectra have shown promise as discriminators in self-driving labs for probing chemical reactivity; this could be extended to incorporating IRIS in high-throughput MS screening workflows. As another example, Martens et al. demonstrated the use of IRIS to characterize individual components of complex samples as they are separated using hybrid liquid chromatographic (LC) MS workflows. By comparing the measured "fingerprint" spectra to calculated reference spectra (typically computed using electronic structure theory methods), researchers can precisely differentiate chemically similar moieties, including isomeric species, without the need for chemical standards. IRIS workflows have been applied in lipidomics, proteomics, metabolomics, and small molecule applications; for example, IRIS has shown utility in characterizing sugars, polymers, and environmental pollutants. Maitre et al. have also described the use of IRIS for the structural characterization of post-translational modifications of proteins (e.g., phosphorylation, glycosylation), which often initiate signaling processes for downstream physiological function. The success of IRIS as a tool for improved structural annotation of unknown analytes depends strongly on the quality and speed of the computational tool used to generate reference spectra. The conventional approach to computing reference spectra involves quantum chemical calculation of harmonic vibrational frequencies and integrated intensities followed by empirical scaling to correct for anharmonicity of vibrational potentials. One can improve harmonic frequency predictions by calculating anharmonic corrections, but the cost of these calculations are much higher than those using the harmonic approximation and they scale rapidly with molecular size, requiring substantial time and computational resources for even moderately sized molecules. Moreover, quantum chemical methods compute absorption spectra, which differ subtly from IRIS spectra, where intensities are associated with IR absorption cross section, as well as the efficiency of anharmonic coupling between normal modes (leading to intramolecular vibrational energy redistribution; IVR) and coupling to dissociation thresholds. By enabling a learned understanding of peak position and mode-coupling, deep learning might provide a solution to the challenges of quickly and accurately predicting IRIS spectra. Further, trained machine learning (ML) models can produce predicted IRIS spectra for thousands of molecules in secondsmuch faster than can be achieved by quantum chemical methods for all but the smallest molecules. To date, ML models of IR absorption spectra have been developed using Message Passing Neural Networks (MPNNs), Graph Attention Networks (GATs), graph transformers, and other frameworks. The success of these models is in part due to the large training datasets of absorption IR spectra (tens of thousands) available in online repositories. However, no large compendium of IRIS spectra for model training exists. Consequently, to the best of our knowledge, a predictive model for IRIS spectroscopy is yet to be reported.