Cited by 81Machine Learning in Materials ScienceOrganometallic Complex Synthesis and CatalysisAdvanced Chemical Physics Studies

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

Open access
Maley, Steven M., Kwon, Doo-Hyun, Rollins, Nick, Stanley, Johnathan C., Sydora, Orson L., Bischof, Steven M., Ess, Daniel H.·2020·Chemical Science

Published version of a preprint

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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Integrity signals

Registry

Crossref · 10.1039/d0sc03552a

Cites

103 of 103 references verified0 citation errors · 0 not checkable

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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).”

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Cites

Flagged first, then by citations

6 of the 109 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. Sunoj R. B. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2011 1 920 931
  2. Hogan P. J. and Banks R. L., in History of Polyolefins: The World's Most Widley Used Polymers, ed. R. B. Seymour and T. Cheng, Springer Netherlands, 1986, pp. 103–115
  3. Zahrt A. F. Henle J. J. Rose B. T. Wang Y. Darrow W. T. Denmark S. E. Science 2019 363 6424 10.1126/science.aau5631 30655414 PMC6417887 · DOI · Paper page

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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
Published
2020
Volume
11
Issue
35
Pages
9665-9674
ISSN
  • 2041-6520 (ISSN-L)
  • 2041-6539
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
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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