A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics
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Peer-reviewed
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Article
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Abstract
jats:pAn 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.</jats:p>
Description
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.
Keywords
4014 Manufacturing Engineering, 40 Engineering, 34 Chemical Sciences, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD)
Journal Title
Reaction Chemistry and Engineering
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Journal ISSN
2058-9883
2058-9883
2058-9883
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Publisher
Royal Society of Chemistry (RSC)
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Sponsorship
Pfizer (Unassigned)
Cambridge Trust (Unassigned)
China Scholarship Council (Unassigned)
Cambridge Trust (Unassigned)
China Scholarship Council (Unassigned)