Model Performance RP Methods. Models were trained using five-fold cross validation (c = 5) to optimize model configuration. Figure shows a summary of the chromatographic metadata used in encoding, where encoding formalisms are discussed in more detail in the Methods section. The best performing Graphormer-RT model (see Figure ) achieved a test mean average error (MAE) of 29.3 ± 0.6 s (c = 5). Although highly imperfect due to differences in number of retention times, methods, etc., we compare our results to models trained on only Figure . A summary of chromatographic method 0153 from RepoRT. The column length (l), the column inner diameter (ID), the particle size (PS), the dead time (t0), and the flow rate (fl) are characteristic method parameters. RTs and inflection points are plotted atop the gradient in red and green, respectively. the METLIN SMRT dataset as a coarse benchmark for our "method-independent" results. Table provides a comparison between several published models trained on the METLIN SMRT dataset with their associated cross-validated MAEs. If we compare our results to the best performing model (i.e., the MPNN work by Osipenko et al.), which reported a MAE = 32.1 ± 0.6 s (c=5), Graphormer-RT achieves predictions that are 9.5% or 4.7σ improved, well beyond the 99.9% confidence interval threshold. This substantial improvement is achieved while also making similarly accurate predictions for 190 other LC methods that are not considered by previous works. If we examine our model's test performance on only the SMRT dataset, we achieve test set error of MAE = 37.7 s (n = 7,814), which suggests that the improved generalization of Graphormer-RT comes at the expense of prediction accuracy for the SMRT library. Figure depicts ML correlation diagrams for RP RT prediction. The correlation plot for the best performing Graphormer-RT split is shown in panel i, and panels ii-vi provide color coding to indicate column manufacturer, column length, t0, mobile phase B, and flow rate, respectively. Figure shows that in our RP dataset, the most common manufactures are Waters and Agilent, where prediction accuracy on Agilent systems (MAE = 37.7 s, n = 4,313) is much poorer than on Waters systems (MAE = 19.8 s, n = 7,821). Whether these differences are related to the hardware, methods, or the chemicals systems, is unknown. Figure shows that Graphormer-RT is slightly better at predicting methods using a methanol (MeOH) organic phase (MAE = 28.9 s) rather than acetonitrile (ACN) (MAE = 26.6 s). Figure shows how methods with smaller t0 typically have shorter RTs and are better predicted (t0 < 1.0 min, n = 5,789, MAE = 16.6 s) than those with large t0 (t0 ≥ 1.0 min, n = 8,478, MAE = 36.8 s). Figure shows a similar but inverted effect, where low-flow rate (fl) systems, which typically have longer retention times, exhibit poorer prediction accuracy (fl < 0.4 mL/min, n = 9,604, MAE = 36.0 s) compared to high flow rate systems (fl ≥ 0.4 mL/min, n = 4,663, MAE = 13.3 s). is mapped into a graph by the Atomic Feature Encoder (3) for node/atom embeddings and our combinatoric encoding scheme for edge encodings. Simultaneously, chromatographic metadata is passed into float (1) and integer (2) chromatography encoders, which generate learned, dense, representations that are added together and stored in the global chromatography node (G). Molecular graphs are then passed to Graphormer (5), whose centrality, spatial, and node encodings are illustrated alongside the model architecture. The learned molecular representation generated by Graphormer is aggregated into the mCLS token and decoded via the output MLP (6), producing the final RT prediction.