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

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., Özcan, E., & 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.