Labes, R, Bourne, RA orcid.org/0000-0001-7107-6297 and Chamberlain, TW orcid.org/0000-0001-8100-6452 (Cover date: July / August 2020) Automated reaction optimisation in continuous flow. Chemistry Today, 38 (4). ISSN 0392-839X
Abstract
This article highlights some of the recent advances in the area of optimising chemical synthesis. We explore how continuous flow chemistry, coupled with automation and optimisation techniques, can constitute a powerful tool that enables searches in larger chemical spaces and can assist delivering better methods faster. The combination of methods like Design of Experiment and local and global optimisation algorithms, such as SIMPLEX and Bayesian approaches, can further enhance the information gathered while optimising a method. Coupling these methods with intelligent, cloud based, automated platforms, enables a holistic approach to optimising chemical synthesis that combines chemistry, engineering and informatics.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | Protected by copyright. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | automation, FLOW CHEMISTRY, machine learning, Organic Synthesis, Peer Reviewed, Self-optimisation |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds) > Inorganic Chemistry (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R032807/1 Royal Academy of Engineering RCSRF1920\9\38 |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Sep 2020 13:53 |
Last Modified: | 06 Jan 2022 17:01 |
Published Version: | https://www.teknoscienze.com/tks_article/automated... |
Status: | Published |
Publisher: | TKS TeknoScienze |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165061 |