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

TY - JOUR

T1 - Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis

T2 - towards improving synthetic chemistry and accelerating mechanistic understanding

AU - Smith, Stuart C.

AU - Horbaczewskyj, Christopher S.

AU - Tanner, Theo F.N.

AU - Walder, Jacob J.

AU - Fairlamb, Ian J.S.

N1 - 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).

PY - 2024/5/24

Y1 - 2024/5/24

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85194157811&partnerID=8YFLogxK

UR - https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00249g

U2 - 10.1039/d3dd00249g

DO - 10.1039/d3dd00249g

M3 - Review article

AN - SCOPUS:85194157811

SN - 2635-098X

JO - Digital Discovery

JF - Digital Discovery

ER -