Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards in silico Catalyst Optimisation
Abstract
An alternative top‐down concept for searching for homogeneous catalysts is introduced. In this approach, three multi‐dimensional spaces are considered. These represent the catalysts, the descriptor values (e.g., cone angle, lipophilicity indices), and the figures of merit (e.g., turnover frequency, enantiomeric excess, or product selectivity), respectively. By generating and connecting these spaces, it is possible to screen virtual catalyst libraries and indicate regions in the catalyst space where good catalysts are likely to be found. The generation of the catalyst space from simple building blocks is presented and the application of this approach is demonstrated for two cases: predicting the bite angle in a library of 600 ligand‐Rh complexes, and predicting the linear : branched aldehyde product ratio that these 600 catalysts would give in the hydroformylation of 1‐octene. In the latter case, the model is first trained on a set of 39 octene hydroformylation reactions. The limitations and applications of this concept are discussed.
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Although descriptors and representations have evolved with the increasing use of more sophisticated ML models in recent years, demonstrations in the early 2000s bear striking similarity to many modern applications.
“Although descriptors and representations have evolved with the increasing use of more sophisticated ML models in recent years, demonstrations in the early 2000s (5-8, 42) bear striking similarity to many modern applications (32,43,(72)(73)(74).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [42]
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- Authors
- Hageman, J. A., Westerhuis, J. A., Frühauf, H., & Rothenberg, G.
- Affiliations
- University of Amsterdam (NL)
- Corresponding author
- Rothenberg, Gadi
- Venue
- Advanced Synthesis & Catalysis
- Publisher
- Wiley
- Published
- 2006-02
- Volume
- 348
- Issue
- 3
- Pages
- 361-369
- ISSN
- 1615-4150 (ISSN-L)
- 1615-4169
- OpenAlex
- W2140942564
- Cited by
- 61
- References
- 36
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 1 · 2022: 3 · 2021: 1 · 2020: 3 · 2019: 5 · 2018: 2 · 2017: 5 · 2015: 3 · 2014: 4 · 2013: 2 · 2012: 5
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advanced Synthesis & Catalysis (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Organometallic Complex Synthesis and Catalysis
- Physical Sciences › Chemistry › Inorganic Chemistry › Asymmetric Hydrogenation and Catalysis
- Keywords
- cone angle, turnover frequency, enantiomeric excess, product selectivity
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