Automation and AI-Powered Prediction in Chromatographic Separation

values to CC retention volumes. Multimodal frameworks extend these principles to GC, combining molecular features with heating programs to predict retention under dynamic conditions. For HPLC enantioseparation, chirality-aware graph neural networks capture subtle stereochemical differences and, when coupled with uncertainty quantification, yield separation probabilities that mirror experimental decision-making. This Account focuses on the development of a unified framework for AI-assisted chromatography, highlighting advances in data acquisition, feature engineering, algorithmic design, and cross-scale modeling. Together, these developments chart a path toward universal chromatographic predictors─tools that are accurate, interpretable, and transferable across methods. By closing the loop with automated experimentation, they lay the foundation for predictive and programmable chromatography capable of accelerating discovery and enhancing reproducibility across the chemical sciences.