Figure compares the predictions for nine molecules in the test split with their respective experimental spectrathree for each charge state. Figures show predictions that are well below the average SIS, Figures show predictions of average accuracy, and Figures show predictions with above average SIS scores. In general, Graphormer-IRIS performs best for organic molecules that have common organic functional groups/structural motifs (e.g., carboxylic acids, ethers). However, given the relatively sparse chemical diversity of the IRIS dataset, we cannot make any comprehensive claims regarding broad molecular generalizability. We also acknowledge that a limitation of the small experimental dataset is that some species contain similar scaffolds (e.g., the agrochemical depicted in Figure ). In future work, splitting via a Murcko scaffold may be useful to assess the extent that this structural similarity impacts model performance. The below average IRIS spectral predictions shown in Figure -C highlight that the underrepresentation of functional groups in the IRIS dataset impacts model predictions even if similar features are well represented in pretraining datasets (e.g., IR, DFT). This effect is most obvious in the IRIS spectra of 4-bromo aniline(Figure ) and ( ), where Graphormer-IRIS predicts poorly the C-Br, C-Cl, and C-F stretching frequencies at ca. 600-800 cm -1 . Although halogenated species with similar stretching frequencies are represented in the experimental IR absorption database that was used for pretraining, these frequencies are poorly represented and predicted in the IRIS dataset. These prediction errors indicate that the IRIS dataset would benefit from expansion to improve the representation and diversity of chemical moieties. Charge State Encodings. We introduce a global charge node to all molecular graphs for IRIS spectra (see Supporting Information: Charge Encodings) so that Graphormer-IRIS could obtain a contextual understanding of molecular charge. While training on IRIS spectra without a description of charge, Graphormer-IRIS yields 𝑆𝐼𝑆 𝜇 = 0.4749 ± 0.0228 for predictions of IRIS spectra (Table ; row #2). Upon transfer learning using the IRIS dataset but signifying that the molecules are in the gas phase using the global node (and the encoding from the Graphormer-IR study), scores improve to 𝑆𝐼𝑆 𝜇 = 0.5491 ± 0.0329 (Table ; row #3). Introducing an explicit global node description of molecular charge state yielded 𝑆𝐼𝑆 𝜇 = 0.6259 ± 0.0400 (Table ; row #4), which is ~50% improved over the naïve Graphormer-IR evaluation. Figure further emphasizes the importance of an explicit, contextual, global description of charge for predicting IRIS spectra generated from our ML architecture. Figure and show the IRIS spectra for phenylacetylglycine as measured and predicted for the deprotonated, protonated, and sodiated charge states, respectively. Similarly, Figure panels D, E, and F show the measured and Graphormer-IRIS spectra for L-aspartic acid in the same charge states. By inspection, Graphormer-IRIS clearly captures the dramatic differences in IRIS spectra as a function of charge state, yielding high-quality predictions. In the case of phenylacetylglycine, several vibrational bands are at similar wavenumbers from one charge state to another, such as those associated with the C=C aromatic stretching frequency at ~1,650 cm -1 , but the IRIS spectrum of the deprotonated species (Figure ) exhibits fewer peaks than the protonated and sodiated species. Furthermore, the IRIS spectrum of protonated phenylacetylglycine (Figure ) exhibits relatively intense bands below 750 cm -1 , which are very weak or absent from the spectra of the sodiated and deprotonated charge states. The IRIS spectrum for the deprotonated charge state of L-aspartic acid (Figure ) demonstrates another interesting phenomenon that is captured by Graphormer-IRISmolecules that contain multiple hydrogen bonding moieties can exhibit significant broadening of some spectral features. Interestingly, this band broadening is not observed experimentally for the protonated or sodiated forms of L-aspartic acid (Figures and), and Graphormer-IRIS also predicts these spectra reasonably well. For L-aspartic acid (Figure ), protonation occurs on the amine moiety and an intramolecular hydrogen bond can form to either of the carboxyl groups. Due to the asymmetry of the amine group along the carbon backbone, the C=O vibrational frequencies of the different hydrogenbonded conformers are not equivalent, and one observes a pair of peaks at ca. 1,750 cm -1 . Graphormer-IRIS successfully captures this subtle interaction, likely owing to the robust pretraining steps. Moreover, Graphormer-IRIS can capture the variable red shifting of the carboxylic acid (Figure : ca. 1,780 cm -1 , Figure : ca. 1,736 cm -1 ; Δ𝜈 = 44 cm -1 ) and amide (Figure : ca. 1,682 cm -1 , Figure : ca. 1,650 cm -1 ; Δ𝜈 = 24 cm -1 ) stretching frequencies upon sodiation. The ability to capture this subtle effect is a testament to the model's contextual understanding of charge provided by the global node. Transfer Learning Strategies. Given the relative paucity of the IRIS spectral library (n = 312), the success of Graphormer-IRIS hinges on effectively transferring knowledge from models trained on IR absorption and DFT-computed spectra. As discussed in the Supporting Information:Transfer Learning, transfer learning can be seen through the lens of human learning, where analogies (e.g., learning to eat yogurt compared to learning to eat soup) and easier versions of tasks (e.g., walking compared to running) can improve model performance. In the context of IRIS, the task of predicting IR spectra for neutral species (i.e., Graphormer-IR's large training set) can be thought to teach the model structure-to-vibration analogies. The DFT spectra data set, on the other hand, represents an "easier" version of gas phase ion spectra (i.e., harmonic modes, no noise) that allows learning on a gradient. Finetuning the best-performing Graphormer-IR model on IRIS spectra (i.e., no intermediate DFT pretraining step) resulted in a performance increase of nearly 50%; SIS improved from 𝑆𝐼𝑆 𝜇 = 0.4219 ± 0.0337 (Table ; row #1) to 𝑆𝐼𝑆 𝜇 = 0.6259 ± 0.0400 (Table ; row #4). We also found that freezing the first layer of the graph feature encoder offered a small (but not statistically significant) improvement in model performance 𝑆𝐼𝑆 𝜇 = 0.6336 ± 0.0396 (Table ; row #5). Moreover, freezing the graph feature encoder offered no further improvement to model performance, yielding 𝑆𝐼𝑆 𝜇 = 0.6285 ± 0.0485 (Table ; row #6).