Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization
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Abstract
The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science.
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Identifying key descriptors or features in a structure-property relationship mapping has been demonstrated as an approach to revealing design principles and enabling iterative design.
“Identifying key descriptors or features in a structure-property relationship mapping has been demonstrated as an approach to revealing design principles (18,22,32,52) and enabling iterative design (70).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [70]
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- Authors
- Maley, S. M., Kwon, D.-H., Rollins, N., Stanley, J. C., Sydora, O. L., Bischof, S. M., & Ess, D. H.
- Affiliations
- Brigham Young University (US)
- Chevron Phillips Chemical (United States) (US)
- Corresponding author
- Bischof, Steven M.
- Ess, Daniel H.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2020
- Volume
- 11
- Issue
- 35
- Pages
- 9665-9674
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC8161675
- OpenAlex
- W3081089118
- Cited by
- 81
- References
- 109
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 12 · 2023: 13 · 2022: 17 · 2021: 19 · 2020: 1
- FWCI
- 4.87
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Chemical Science (publishedVersion, cc-by-nc)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by-nc-nd)
- Funders
- Brigham Young University
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Organometallic Complex Synthesis and Catalysis
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- chromium catalysts, ethylene oligomerization, catalyst design, random forest regression, ligand descriptors, homogeneous catalysis, molecular catalysts
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