  We present an innovative of artificial intelligence with column
chromatography, aiming to resolve inefficiencies and standardize data
collection in chemical separation and purification domain. By developing an
automated platform for precise data acquisition and employing advanced machine
learning algorithms, we constructed predictive models to forecast key
separation parameters, thereby enhancing the efficiency and quality of
chromatographic processes. The application of transfer learning allows the
model to adapt across various column specifications, broadening its utility. A
novel metric, separation probability ($S_p$), quantifies the likelihood of
effective compound separation, validated through experimental verification.
This study signifies a significant step forward int the application of AI in
chemical research, offering a scalable solution to traditional chromatography
challenges and providing a foundation for future technological advancements in
chemical analysis and purification.
