DFT Spectra Pretraining. The DFT spectral library consists of spectra generated from ionic structure ensembles that have been explicitly (de)protonated/sodiated and then optimized using DFT methods. More details are available in the Supporting Information: Computed DFT Spectra section. Because the original Graphormer-IR models have minimal awareness of charge state (See Supporting Information: Charge Encodings), we used the DFTcomputed spectral library developed in reference 28 to improve the model's understanding of the charge state encodings. Since the DFT spectra dataset contains ionic conformers, we hypothesized that models pretrained on this dataset might gain a sense of (de)protonation/sodiation. Of course, the potential downside of using DFT-computed spectra in training purposes is that peak positions, which are computed within the harmonic oscillator approximation and corrected with a uniform scaling factor, are possibly different from those observed experimentally and so might be detrimental to model accuracy. When pretraining on the DFT spectra (after already pretraining on the experimental IR library for neutral species), then finetuning on experimental IRIS spectra, the model similarity score improves to 𝑆𝐼𝑆 𝜇 = 0.6811 ± 0.0339 (Table ; row #9). This accuracy is a substantial improvement over the DFTprediction performance benchmark of 𝑆𝐼𝑆 𝜇 = 0.5616. The improvement in performance as a function of DFT pretraining suggests that the difference between experimental and DFT-computed peak positions might not be especially detrimental to model learning. When transferring knowledge of vibrational frequencies from the model for predicting experimental IR spectra to the DFTrefined model and, ultimately, to the experimental IRIS model, the transformer architecture might be better able to focus on robust anharmonic frequency correction and IRIS band intensity correction in the final step, having already gained some understanding for the impact of charge state via the intermediate DFT spectra pretraining step. To explore this notion, we examined model predictions made at intermediate stages of transfer learning. Figure highlights an additional explanation for the positive effect of the DFT spectra finetuning procedure, which might act as a "cleaning" step in our workflow. Experimental IR spectra can contain noise and impurities, leading to extemporaneous signals that the model can learn during the training phase and subsequently introduce into predictions. By pretraining on DFT spectra before training on the IRIS spectra, the model learns to eliminate these "noisy" peaks from predictions and consider only "real" vibrational features. We also trained an uninitialized model (i.e., one not pretrained on absorption IR spectra of neutral molecules) on only the DFT library using the global charge encoding. Doing so yields SISμ = 0.4777 ± 0.0329 (Table ; row #7). Further finetuning of this model (as opposed to evaluating) on the experimental IRIS spectra improves the similarity score to SISμ = 0.5080 ± 0.0434 (Table ; row #8). This result indicates that training solely on the DFT spectra is not sufficient to yield an accurate IRIS model. Instead, it is necessary to first pretrain on experimental IR spectra for neutral species. UMAP: Uniform Manifold Approximation and Projection for Dimensionality Reduction. To explore why pretraining steps are necessary to achieve optimal model performance (i.e., what knowledge is passed to Graphormer-IRIS from transfer learning), we perform dimensionality reduction on model embeddings. Figure shows the UMAP projection (See Supporting Information: Universal Manifold Approximation and Projection) of node embeddings generated by Graphormer-IR, illustrating some of the "chemical first principles" that might be passed to Graphormer-IRIS. This figure exhibits well-defined clusters based on atom type and hybridization, demonstrating a sophisticated understanding of local chemical environments and allowing us to draw three conclusions. First, the clustering of specific node level embeddings demonstrates that the Graphormer-IR model (i.e., the first pretraining step) derives an understanding of the emergent "concept" of functional groups without explicit annotation of these geometric motifs and their properties. For instance, in Figure , the embeddings of both the sp 2 oxygen (light and sp 2 nitrogen (light blue) moieties are clustered in the vicinity of one another on the UMAP plot. Secondly, the embeddings for moieties with dissimilar encodings but similar vibrational frequencies tend to be clustered. For example, the halogens (R-F R-Cl, R-Br, R-I) which produce similar vibrational frequencies (ca.550 -850 cm -1 ) have similar embeddings in the UMAP space (see Figure ). Likewise, 1,3 dipolar species including azides (R-N3), isocyanates (R-N=C=O), and thiocyanates (R-N=C=S) have similar embeddings, which is likely due to the model's understanding of the similar stretching frequencies (ca. 2,250 cm -1 ). Lastly, we observe that the UMAP x-dimension acts as a proxy for the extent of electron delocalization in each molecular ion. At the coarsest grain, the embeddings in the most negative x-direction consist largely of sp 2hybridized carbon moieties and highly conjugated systems like pyridines, nitro groups, and aromatic species. Molecules in the most positive x-direction largely consisting of alkanes, silicon derivatives, and halogenated species. At a finer grain, this trend continues where conjugated R-N=C=O moieties (e.g., R = benzyl groups) have embeddings shifted to the "delocalized" x-direction, relative to those that are not conjugated (e.g. R = alkyl groups). At their core, these phenomena speak to the derived "chemical intuition" of the foundational Graphormer-IR model and demonstrate the analogous chemical principles and structure-to-vibrational frequency relationships that are being passed to Graphormer-IRIS in the transfer learning process.