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ML meets MLn: machine learning in ligand promoted homogeneous catalysis

Hirst, Jonathan D.; Boobier, Samuel; Coughlan, Jennifer; Streets, Jessica; Jacob, Philippa L.; Pugh, Oska; Özcan, Ender; Woodward, Simon

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Authors

Samuel Boobier

Jennifer Coughlan

Jessica Streets

Philippa L. Jacob

Oska Pugh

Profile image of ENDER OZCAN

ENDER OZCAN ender.ozcan@nottingham.ac.uk
Professor of Computer Science and Operational Research

Profile image of SIMON WOODWARD

SIMON WOODWARD simon.woodward@nottingham.ac.uk
Professor of Synthetic Organic Chemistry



Abstract

The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised. We focus on the understanding and implementation of key concepts, which serve as conduits to more advanced chemical machine learning literature, much of which is (presently) outside the area of homogeneous catalysis. Potential pitfalls in the ‘workflow’ procedures needed in the machine learning process are identified and all the examples provided are in a chemical sciences context, including several from ‘real world’ catalyst systems. Finally, potential areas of expansion and impact for machine learning in homogeneous catalysis in the future are considered.

Citation

Hirst, J. D., Boobier, S., Coughlan, J., Streets, J., Jacob, P. L., Pugh, O., …Woodward, S. (2023). ML meets MLn: machine learning in ligand promoted homogeneous catalysis. Artificial Intelligence Chemistry, 1(2), Article 100006. https://doi.org/10.1016/j.aichem.2023.100006

Journal Article Type Article
Acceptance Date Jul 10, 2023
Online Publication Date Jul 18, 2023
Publication Date 2023-12
Deposit Date Jul 31, 2023
Publicly Available Date Aug 1, 2023
Journal Artificial Intelligence Chemistry
Electronic ISSN 2949-7477
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 1
Issue 2
Article Number 100006
DOI https://doi.org/10.1016/j.aichem.2023.100006
Public URL https://nottingham-repository.worktribe.com/output/23005752
Publisher URL https://www.sciencedirect.com/science/article/pii/S2949747723000064?via%3Dihub
Additional Information This article is maintained by: Elsevier; Article Title: ML meets MLn: machine learning in ligand promoted homogeneous catalysis; Journal Title: Artificial Intelligence Chemistry; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.aichem.2023.100006; Content Type: article; Copyright: © 2023 The Authors. Published by Elsevier B.V.

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