Topological Mapping of Bidentate Ligands: A Fast Approach for Screening Homogeneous Catalysts
Abstract
The challenge of predicting the catalytic properties of large libraries of homogeneous catalysts is introduced. A new concept is presented that combines fundamental chemical topology principles with linear and non‐linear statistical analysis. These models can predict key properties of bidentate ligand‐metal complexes, namely the ligand bite angle and the backbone flexibility, without computing any 3D structural parameters and without using any force fields or any quantum mechanics. The model's performance is demonstrated on a set of 80 biphosphine and biphosphite complex crystal structures. With non‐linear methods, the prediction accuracy is 93% for bite angles and 90% for flexibilities. The link between the descriptors and the ligand structures, and the possibilities that this approach opens in the search for new homogeneous catalysts 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 [7]
Low-cost, topological, three-dimensional, one-hot chemical encoding, and geometric descriptors are often employed for this purpose in transition-metal chemistry.
“Low-cost, topological (5, 7, 32, 92-94), three-dimensional (95-100), one-hot chemical encoding (92, 101), and geometric (e.g., for MOF pores) (102-104) descriptors are often employed for this purpose in transition-metal chemistry.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [7]
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References 24 · Cited by 49
- Authors
- Burello, E., & Rothenberg, G.
- Affiliations
- University of Amsterdam (NL)
- Corresponding author
- Rothenberg, Gadi
- ORCID
- Rothenberg, Gadi
- Venue
- Advanced Synthesis & Catalysis
- Publisher
- Wiley
- Published
- 2005-12
- Volume
- 347
- Issue
- 15
- Pages
- 1969-1977
- ISSN
- 1615-4150 (ISSN-L)
- 1615-4169
- OpenAlex
- W1980052013
- Cited by
- 49
- References
- 24
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 2 · 2021: 2 · 2019: 1 · 2017: 2 · 2015: 2 · 2014: 2 · 2013: 3 · 2012: 5
- FWCI
- 2.64
- 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 › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Organic Chemistry › Organometallic Complex Synthesis and Catalysis
- Keywords
- bidentate ligands, homogeneous catalysts, chemical topology, ligand bite angle, backbone flexibility, metal-ligand complexes, catalyst screening
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