Infrared (IR) radiation was discovered by William Herschel in 1800, unlocking a new method of molecular analysis by harnessing the interaction between molecules and IR radiation. Herschel discovered that different molecules absorb and emit distinct frequencies, allowing for molecular identification and characterization. Infrared spectra were not measured until 1881, when Abney and Festing were able to capture vibrational frequencies used photographic plates. The first breakthrough towards "modern" IR spectroscopy came in 1905 when William Coblentz published a large library of molecular spectra recorded between 1,000 and 16,000 nm that allowed researchers to fingerprint specific molecules using frequencies that could be related to molecular structure. Today, IR spectroscopy provides an invaluable tool for identifying compounds and monitoring changes in analyte matrices owing to the direct relationship between molecular vibrational energies and molecular structure. For example, IR spectroscopy is employed in the identification and quantification of impurities or degradation products in drugs to determine product quality and to meet stringent regulatory requirements. In biochemistry, protein structure (alpha helices, beta sheets, and random coil regions), conformational changes, and interactions (e.g., hydrogen bonding) can all be monitored with IR spectroscopy. Analyzing polymer infrared spectra aids in understanding the chemical composition, chain length, and degree of polymerization. In inorganic chemistry, this technique aids in identifying metal complexes, coordination compounds, and minerals. Analyzing vibrational modes (and their relative shifts) and absorption frequencies provides insights into their structures, bonding, and reactivity. Forensic science employs IR spectroscopy in evidence analysis, characterizing substances like drugs, fibers, and paints. In the domain of cultural heritage preservation, IR spectroscopy plays a significant role in analyzing pigments, binders, and coatings on artworks and artifacts to support conservation and restoration efforts. Given such wide practical application, a means to consistently predict accurate experimental infrared spectra for experimental benchmarking or pre-screening is highly desirable. Current ab initio methods have inherent limitations when it comes to accurately calculating infrared (IR) spectra. Oftentimes, they rely on the harmonic oscillator approximation for computing vibrational spectra since this approach usually yields reasonable results in a relatively short time. However, accurate spectral predictions require anharmonic corrections that are computationally expensive. These corrections are especially important for accurately modelling systems that exhibit highly anharmonic vibrational modes (e.g., shared protons and H-bonded systems). Calculating typical anharmonic infrared spectra of small organic molecules using ab initio methods can take days to weeks, scaling rapidly with the number of heavy atoms. Frequencies in both harmonic and anharmonic calculations are also typically adjusted by a method-specific scaling factor. Previous work has shown that variation in these scaling factors can lead to discrepancy in peak positions of as high as 30 cm -1 . Additionally, environmental factors like solvent interactions or temperature effects complicate spectra are also not easily captured by fundamental calculations. Given these difficulties in generalizing calculations for ab initio anharmonic IR spectra, deep learning methods provide an attractive solution because of the speed of evaluation and the ad hoc evaluation of environment (temperature, solvent, etc.) from the dataset. As such, infrared spectra have been utilized in machine learning models in diverse ways. Midand near-IR spectra have been used for classification and prediction of various "real" matrices. This includes but is not limited to the classification of plastic waste, prediction of beer quality (pH, alcohol content, foam content), and the prediction of neonatal respiratory distress biomarker concentrations, . Deep learning attempts to predict IR spectra include the use of multilayer perceptrons (MLPs) , prediction of protein IR spectra, and use of Morgan fingerprints to predict interstellar polycyclic aromatic hydrocarbon (PAH) infrared spectra. MLPs have also been used to enable calculation of infrared spectra by predicting potential energies and dipole moments using symmetry and radial interaction functions. For this study, the most relevant example of deep learning is Chemprop-IR, a study predicting IR spectra from chemical structure using a derivatization of the Directed Message Passing Neural Network (D-MPNN) package Chemprop. Originally conceived in 2019 to predict the output of quantum chemical calculations, MPNNs are a type of graph neural network (GNN) that have proven to be a valuable tool for modeling chemical systems that can be expressed as mathematical graphs. Notably, MPNNs have yielded stateof-the art score in a variety of deep learning tasks in chemistry including prediction of liquid chromatographic retention times , pharmacological properties, and various physicochemical properties. Like any architecture, MPNNs also possess certain limitations. One prominent flaw lies in their ability to capture complex higher-order relationships within chemical systems. MPNNs typically rely on a fixed number of message-passing steps, which can restrict their capacity to capture intricate long-range interactions and dependencies. Chemical systems often exhibit long-range interactions that play a vital role in determining properties and behaviors. In the prediction of infrared spectra, this could be relevant in calculating the "true" anharmonicity of a particular vibrational frequency, which is influenced by global molecule electronics, capturing solvent effects, or considering long-range interactions like hydrogen bonding.