Automation and AI-Powered Prediction in Chromatographic Separation

Abstract

ConspectusChromatography remains one of the most versatile separation technologies in chemistry, spanning thin-layer chromatography (TLC) for rapid analysis, column chromatography (CC) for purification, gas chromatography (GC) for volatile analytes, and high-performance liquid chromatography (HPLC) for precise and enantioselective separations. Despite its centrality, the development of chromatographic methods has long relied on empirical trial-and-error and tacit practitioner knowledge, making reproducibility and systematic optimization difficult. The convergence of laboratory automation and artificial intelligence (AI) is now reshaping this landscape. Automated platforms generate large-scale, standardized data sets, while machine-learning models capture quantitative relationships between molecular structures, chromatographic conditions, and retention outcomes. Crucially, by embedding mechanistic constraints─such as polarity-driven adsorption, programmed heating effects, or stereochemical recognition─models transcend black-box prediction and deliver interpretable insights into separation mechanisms. Our research illustrates how chromatography can be transformed into a predictive science through the integration of automation, machine learning, and cross-method transfer. Robotic TLC and CC systems provide reproducible polarity data that inform predictive models and even transferable equations linking TLC Rf 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.