Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge

Natalie Fey*, Jason M. Lynam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Although computational contributions to the understanding of organometallic homogeneous catalysts have become fairly routine, a step-change in the application of computational methods would be to achieve efficient, robust, and reliable prediction of the outcome of catalytic transformations. While we concur that there have been a number of recent promising advances in the interactions between computational and experimental mechanistic studies, the mapping of reactivity space remains incomplete and large-scale studies have to make limiting assumptions which restrict their transferability. Close synergies between characterization and analysis techniques which are integrated with computational data, along with data capture, curation, and exploitation, are vital and develop our understanding of all aspects of the catalytic pathways (including activation and deactivation) and allow the continual refinement of mechanistic understanding, challenged by testing predictions experimentally. Here we review recent examples to formulate a protocol for such interactions. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Structure and Mechanism > Reaction Mechanisms and Catalysis Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory.

Original languageEnglish
Article numbere1590
Number of pages23
JournalWiley Interdisciplinary Reviews: Computational Molecular Science
Issue number4
Early online date23 Nov 2021
Publication statusPublished - Jul 2022

Bibliographical note

Funding Information:
We have been fortunate to work with many talented students, collaborators, and mentors, and their contributions to our understanding of this area are gratefully acknowledged. While no specific research project is reported here, the authors' collaboration has been supported by funding from the UK's Engineering and Physical Sciences Research Council, the Dial‐a‐Molecule Network, the University of York's Pump‐Priming Fund and the generous availability of computing resources through Bristol's Center for Computational Chemistry. These opportunities are gratefully acknowledged.

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  • catalysis
  • computational
  • machine learning
  • mechanism
  • organometallic

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