Open AccessA Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics
Author(s) -
Jeraal Mohammed I.,
Sung Simon,
Lapkin Alexei A.
Publication year - 2021
Publication title -
chemistry ‐ methods
Language(s) - English
Resource type - Journals
ISSN - 2628-9725
DOI - 10.1002/cmtd.202000044
Subject(s) - multi objective optimization , computer science , process (computing) , matlab , yield (engineering) , pareto principle , process optimization , interface (matter) , flow (mathematics) , mathematical optimization , machine learning , engineering , mathematics , chemical engineering , materials science , bubble , maximum bubble pressure method , parallel computing , metallurgy , operating system , geometry
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.