Computational Mapping of Dirhodium(II) Catalysts

Adam Green , Christopher Tinworth, Stuart Warriner, Adam Nelson*, Natalie Fey*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)
97 Downloads (Pure)


The chemistry of dirhodium(II) catalysts is highly diverse, and can enable the synthesis of many different molecular classes. A tool to aid in catalyst selection, independent of mechanism and reactivity, would therefore be highly desirable. Here, we describe the development of a database for dirhodium(II) catalysts that is based on the principal component analysis of DFT‐calculated parameters capturing their steric and electronic properties. This database maps the relevant catalyst space, and may facilitate exploration of the reactivity landscape for any process catalysed by dirhodium(II) complexes. We have shown that one of the principal components of these catalysts correlates with the outcome (e.g. yield, selectivity) of a transformation used in a molecular discovery project. Furthermore, we envisage that this approach will assist the selection of more effective catalyst screening sets, and, hence, the data‐led optimisation of a wide range of rhodium‐catalysed transformations.
Original languageEnglish
Pages (from-to)2402-2409
Number of pages8
JournalChemistry - A European Journal
Issue number7
Early online date12 Jan 2021
Publication statusPublished - 1 Feb 2021

Bibliographical note

Funding Information:
The calculations reported were undertaken on ARC3, part of the High‐Performance Computing facilities at the University of Leeds, UK. We thank the Centre for Computational Chemistry at the University of Bristol for providing access to additional computing resources; EPSRC (EP/N025652/1) and GSK for funding; and Dr George Karageorgis for helpful discussions.

Publisher Copyright:
© 2020 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH


  • Computational chemistry
  • Homogeneous catalysis
  • Rhodium
  • Ligands
  • Data-led prediction


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