To further investigate the generalization of Graphormer-RT, we predicted RTs for LC methods had not been used to train the model. This approach provides information regarding the extent to which the model is "memorizing" the LC method scaling factors from the training set (task ii; vide supra). The results for "heldout" external LC methods are reported in Table . Additional details are available in Supplementary Figures -S9. For methods 0127 and 0275, Graphormer-RT generalizes well, achieving MAEs of 43.9 ± 3.3 s and 42.7 ± 6.2 s (c = 5), respectively. While slightly worse than the accuracy of the global test set that was randomly split from the training set LC methods, the sub-minute accuracies inspire Figure . Test set analysis for the best-performing Graphormer-RT models for RP predictions showing performance (i) as a function of chromatographic conditions including manufacturer (ii), column length (iii), t0 (iv), organic phase composition (v), and flow rate (vi) confidence that Graphormer-RT is generalizing from chromatographic first principles. In contrast, Graphormer-RT fails to generalize (MAE = 596.8 ± 12.7 s) for method 0029. This poor performance likely arises owing to the complexity of the gradient profile for method 0029 (see Figure ), and it suggests that our formalism for gradient profiles may need to be improved for specific cases. To further explore the value of method-specific training, we proportionally reincorporated the held-out external methods back into the training-validation-test splits. Although an imperfect comparison to the "true" external evaluation due to differences in test set size (n = 215 vs. n = 22), this experiment provided a coarse estimation of the improvement offered by training on specific methods. For LC method 0029, introducing examples to the training set markedly improved test performance (by nearly an order of magnitude; MAEincorporated = 65.6 ± 21.1 s versus MAEheld-out = 596.8 ± 12.7 s). Although the results for method 0029 are still worse than the average method performance, this outcome indicates that Graphormer-RT has learned the scaling factors for method 0029 reasonably well. From the experimental perspective, researchers that are employing complex or uncommon LC methods could consider "calibrating" Graphormer-RT by incorporating measured RTs from a calibration set into the training library. HILIC Results. Our best performing HILIC model achieved MAE = 42.4 ± 2.9 s (c = 5, see Table ). This model, which was first finetuned on the RP dataset, was trained using a library of 4,373 RTs associated with 49 HILIC methods. Interestingly, the improvement obtained from transfer learning was relatively small; uninitialized models achieve MAE = 44.5 ± 1.3 s. This result suggests that the RP and HILIC prediction tasks may be sufficiently dissimilar that transfer learning is not useful or that the HILIC training set size is sufficiently large and diverse to enable generalization. Figure provides correlation diagrams for HILIC predictions, like those discussed earlier for RP methods (i.e., Figures 3). In contrast to the model predictions for RP methods, MAEs for HILIC method predictions are not heteroscedastic. Whether this is a result of effective generalization for HILIC methods by Graphormer-RT or due to the implicit properties of the dataset is not known; a larger HILIC RT library is needed to investigate this further. That said, Graphormer-RT's performance on held-out external HILIC test sets does inspire some confidence in model generalization. As shown in Table , Graphormer-RT achieves MAEs of 83.7 ± 7.3 s, 66.6 ± 7.0 s, and 25.5 ± 2.5 s for Re-poRT HILIC methods 0103, 0283, and 0375, respectively. Regarding method 0375, Graphormer-RT predictions are more accurate by almost 20 s than the average global performance for methods that were included in the training set (MAE = 25.5 ± 2.5 s, see Table ). A possible explanation for this relatively good performance could be that data for a very similar HILIC method is present in the training set. Given a sufficiently diverse set of HILIC methods, we expect that Graphormer-RT is expressive enough to predict RTs for novel methods. To the best of our knowledge, this is the first model that can accurately predict RTs for small molecule HILIC methods, as well as the first framework to generalize HILIC predictions across multiple methods.