Intelligent column chromatography prediction model based on automation and machine learning

Efficient compound separation remains a persistent challenge in synthetic chemistry, with column chromatography serving as a critical purification tool. Traditional methods require extensive expertise and repetitive labor—areas where AI offers significant advantages. This study introduces an AI-driven platform to automate data collection and optimize separation processes. By leveraging deep learning, the system predicts key separation parameters, while transfer learning enables adaptation to diverse column specifications. A novel metric, separation probability (𝑆𝑝), quantifies the likelihood of successful component isolation and has been experimentally validated. The approach enhances precision, reduces manual intervention, and expands the scope of chromatographic applications, offering a more efficient and scalable solution for chemical purification.