Our best performing model (see Table , Ablation 1) employed a global chromatography node with learned embeddings that were generated from gradient features and column features. These features were passed into two separate int/float encoders, achieving a MAE = 28.6 s. The addition of the calculated Tanaka/HSMB parameters decreased model accuracy slightly to MAE = 29.3 s (see Table , Ablation 2), suggesting that these parameters are made redundant by Graphormer-RT's derived understanding of chromatographic separation. The inclusion of a singular chromatography encoder instead of two divided by domain (integer and float) also slightly decreased model accuracy, yielding MAE = 29.9 s (see Table , Ablation 3). It may be that mixing of encoding types obfuscates learning. We also tested ablations that removed gradient features (e.g., %Bstart, inflection points, solvent/pH/additives of both mobile phases) and column features (e.g., diameter, particle size, length, t0, temperature, manufacturer, and United States Pharmacopeial (USP) code). Removal of gradient features (see Table , Ablation 5) resulted in a more substantial reduction in accuracy, MAE = 33.4 s, than did removal of column features (MAE = 32.0 s; see Table , Ablation 4). These results suggest that gradient information may be slightly more important than column information, but both sets of features are needed for optimal model performance. We also tested the removal of all empirical descriptions of the chromatography, instead labeling each method as a discrete class. These encodings were passed through the chromatography encoder and achieved a MAE = 50.9 s (see Table 3, Ablation 6). This large decrease in accuracy compared to that of the best model demonstrates the value of including descriptive empirical method descriptions. If two methods have similar gradients with different mobile phases, models can use the similarity in those empirical descriptions to learn by analogy and more effectively generalize from chromatographic first principles. Finally, we investigated an ablation whereby we removed all method information (see Table , Ablation 7). Even without chromatography descriptors, Graphormer-RT yielded a MAE = 76.8 s. Removal of the chromatographic descriptors is tantamount to limiting learning to only task (i) (i.e., relative molecular interactions), and the fact that even coarse predictions are possible demonstrates the ability of Graphormer-RT to learn interactions from molecular graph structure.