A Machine Learning-Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics

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.