Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis:towards improving synthetic chemistry and accelerating mechanistic understanding

Abstract

Automation technologies and data science techniques have been successfully applied to optimisation and discovery activities in the chemical sciences for decades. As the sophistication of these techniques and technologies have evolved, so too has the ambition to expand their scope of application to problems of significant synthetic difficulty. Of these applications, some of the most challenging involve investigation of chemical mechanism in organometallic processes (with particular emphasis on air- and moisture-sensitive processes), particularly with the reagent and/or catalyst used. We discuss herein the development of enabling methodologies to allow the study of these challenging systems and highlight some important applications of these technologies in problems of considerable interest to applied synthetic chemists.

Metadata

Item Type:	Article
Authors/Creators:	Smith, Stuart C. Horbaczewskyj, Christopher S. Tanner, Theo F.N. Walder, Jacob J. Fairlamb, Ian J.S. https://orcid.org/0000-0002-7555-2761
Copyright, Publisher and Additional Information:	Funding Information: We are grateful to the EPSRC for funding (C. S. H. and I. J. S. F.; EP/S009965/1 and EP/W031914/1) and to the Royal Society for an Industry Fellowship (I. J. S. F.). AstraZeneca and GSK have provided PhD studentship co-funding for S. C. S. and J. J. W. respectively. We thank Prof. Jason Lynam from the Department of Chemistry in York and Dr Neil Scott for their comments and valuable input into this review article. We also thank Dr George Clarke for his input into the potential material that could be included in this review paper. We are grateful to the EPSRC, Royal Society and industry funders (AstraZeneca and GSK) for supporting our research efforts in this area. Publisher Copyright: © 2024 The Author(s).
Dates:	Published (online): 24 May 2024 Accepted: 14 May 2024
Institution:	The University of York
Academic Units:	The University of York > Faculty of Sciences (York) > Chemistry (York)
Funding Information:	Funder Grant number EPSRC EP/S009965/1 EPSRC EP/W031914/1
Depositing User:	Pure (York)
Date Deposited:	07 Jun 2024 13:10
Last Modified:	05 Jan 2025 00:40
Published Version:	https://doi.org/10.1039/d3dd00249g
Status:	Published online
Refereed:	Yes
Identification Number:	10.1039/d3dd00249g
Related URLs:	http://www.scopus.com/inward/record.url?... https://pubs.rsc.org/en/content/articlel...
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:213228

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Description: Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis: towards improving synthetic chemistry and accelerating mechanistic understanding

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Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis:towards improving synthetic chemistry and accelerating mechanistic understanding

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