Cited by 21Radical Photochemical ReactionsOrganometallic Complex Synthesis and CatalysisPhotochemistry and Electron Transfer Studies

Hammett neural networks: prediction of frontier orbital energies of tungsten–benzylidyne photoredox complexes

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Chang, Alexander M., Freeze, Jessica G., Batista, Victor S.·2019·Chemical Science

Abstract

The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten–alkylidyne complexes of the form W(CArR)L 4 X.

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Crossref · 10.1039/c9sc02339a

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45 of 45 references verified0 citation errors · 0 not checkable

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  • In some cases, deep learning models have been developed to predict frontier orbital energies, which can be applied to explain experimental activity of catalysts known to be correlated to such energies.

    “In some cases, deep learning models (e.g., ANNs) have been developed to predict frontier orbital energies (22,43,86), which can be applied to explain experimental activity of catalysts known to be correlated to such energies (86).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [86]

    The Introduction (page unavailable) says the models predict frontier-orbital energies and that a relevant orbital energy is strongly correlated with a complex’s redox potential. The Results and discussion and Conclusions (pages unavailable) use those predictions to identify promising reducing catalysts, but do not show the models explaining experimentally measured catalytic activity. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 2

  1. Rasmussen C. E. and Williams C. K. I., Gaussian Processes for Machine Learning, MIT Press, 2006.
  2. Akaike H. IEEE Trans. Autom. Control 1974 19 716 723

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Authors
Chang, A. M., Freeze, J. G., & Batista, V. S.
Affiliations
  • Yale University (US)
  • Department of Chemistry
Venue
Chemical Science
Published
2019
Volume
10
Issue
28
Pages
6844-6854
ISSN
  • 2041-6520 (ISSN-L)
  • 2041-6539
OpenAlex
W2951654613
Cited by
21
References
45
Citations by year
2026: 2 · 2025: 3 · 2024: 2 · 2023: 8 · 2022: 2 · 2021: 3 · 2020: 1
FWCI
0.59
Type
journal article
Language
en
Open access
Locations
  • Chemical Science (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
Funders
Air Force Office of Scientific Research (FA9550-17-0198)
Topics
  • Physical Sciences › Chemistry › Organic Chemistry › Radical Photochemical Reactions
  • Physical Sciences › Chemistry › Organic Chemistry › Organometallic Complex Synthesis and Catalysis
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Photochemistry and Electron Transfer Studies
Keywords
Hammett parameters, frontier orbital energies, photoredox catalysis, tungsten complexes, substituent effects, redox potential, tetramethylethylenediamine, visible-light photoactivation, ligand substitution effects

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