A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics
Mohammed I. Jeraal, Simon Sung, Alexei A. Lapkin
Abstract
Abstract Self‐optimization of chemical reactions using machine learning multi‐objective algorithms has the potential to significantly shorten overall process development time, providing users with valuable information about economic and environmental factors. Using the Thompson Sampling Efficient Multi‐Objective (TS‐EMO) algorithm, the self‐optimization flow chemistry system in this report demonstrates the ability to identify optimum reaction conditions and trade‐offs (Pareto fronts) between conflicting optimization objectives, such as yield, cost, space‐time yield, and E‐factor, in a data efficient manner. Advantageously, the robust system consists of exclusively commercially available equipment and a user‐friendly MATLAB graphical user interface, and was shown to autonomously run 131 experiments over 69 hours uninterrupted.