A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics
- Dr. Mohammed I. Jeraala
- Dr. Simon Sunga
- Prof. Alexei A. Lapkina,b
- aCambridge Centre for Advanced Research and Education in Singapore Ltd., 1 Create Way, CREATE Tower #05-05, 138602 Singapore
- bDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS UKRead the publication that featured this 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.
Get in touch
For more information on flow chemistry systems and services please use the contact methods below.
Call us on +44 (0)1284 728659 or Email us
The Vapourtec R-Series is, quite simply, unrivalled for flow chemistry
- Flexible |
- Precise |
The R-Series is undoubtedly the most versatile, modular flow chemistry system available today.
The Vapourtec E-Series is the perfect introductory system for flow chemistry
- Robust |
- Easy to use |
The E-Series is a robust and affordable, entry level flow chemistry system designed for reliability and ease of use.