A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics-Reference-Cited by-同舟云学术

A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

Published:2024 Issue: Volume: Page:
ISSN:2058-9883
Container-title:Reaction Chemistry & Engineering
language:en
Short-container-title:React. Chem. Eng.

Author:

Karan Dogancan¹,Chen Guoying¹,Jose Nicholas¹²³,Bai Jiaru²^ORCID,McDaid Paul⁴,Lapkin Alexei A.¹²⁵^ORCID

Affiliation:

1. Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore 138602, Singapore

2. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK

3. Accelerated Materials Ltd, 71-75, Shelton Street, WC2H 9JK London, UK

4. Pfizer Process Development Centre, RCMF, Shanbally, Ringaskiddy, P43 X336, Cork, Ireland

5. Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge CB2 1EW, UK

Abstract

An 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.

Funder

Pfizer

Cambridge Trust

China Scholarship Council

Publisher

Royal Society of Chemistry (RSC)

Subject

Fluid Flow and Transfer Processes,Process Chemistry and Technology,Chemical Engineering (miscellaneous),Chemistry (miscellaneous),Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RE/D3RE00539A

Reference40 articles.

1. D. M.Hodgson , Organolithiums in enantioselective synthesis , Springer Science & Business Media , 2003