A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

Acknowledgements: This work was supported by Pfizer within the Pharma Innovation Programme in Singapore (PIPS) project. The project was hosted by Cambridge Centre for Advanced Research and Innovation in Singapore (CARES) whose lab is supported by the National Research Foundation Project C4T within CREATE Campus. J. Bai acknowledges financial support provided by CSC Cambridge International Scholarship from Cambridge Trust and China Scholarship Council.An automated flow chemistry platform was designed to collect data for a lithium-halogen exchange reaction. The data was used to train a Bayesian multi-objective optimization algorithm to optimize the process parameters and build process knowledge.