Cited by 68Machine Learning in Materials ScienceOrganometallic Complex Synthesis and CatalysisCatalysis for Biomass Conversion

Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage

Open access
Cordova, Manuel, Wodrich, Matthew D., Meyer, Benjamin, Sawatlon, Boodsarin, Corminboeuf, Clémence·2020·ACS Catalysis

Abstract

The increasing urgency to make chemical processes more environmentally friendly while continuing to derive the chemicals required for modern society from renewable resources requires the development of a forthcoming generation of synthetic processes and the catalysts needed to facilitate these reactions. Recently, applications of machine-learning (ML) algorithms involving catalysis have begun to appear with increasing frequency, as they constitute an attractive pathway both for discovering prospective species and identifying trends surrounding catalytic behavior, principally because the number of potential catalysts that can be examined greatly exceeds those found in more traditional experimental or theoretical approaches. Here, we harness a data-driven approach powered by ML in tandem with molecular volcano plots to estimate the activity of over 143,000 homogeneous nickel catalysts bearing phosphine and N-heterocyclic carbene ligands for the reductive C(sp2)–O cleavage reaction in aryl ether compounds, an important step in the degradation of biomass (lignin) into industrially useful feedstock chemicals. Our computational workflow reveals that a vast majority of Ni-phosphine and Ni-carbene catalysts are not ideally tuned to facilitate this reaction. An analysis of those species identified as being the most promising uncovers a clear catalytic design strategy that can be exploited in an experimental setting to enhance the rate of reductive C(sp2)–O cleavage of aryl ether compounds.

Integrity signals

Registry

Crossref · 10.1021/acscatal.0c00774

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67 of 67 references verified0 citation errors · 0 not checkable

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Authors
Cordova, M., Wodrich, M. D., Meyer, B., Sawatlon, B., & Corminboeuf, C.
Affiliations
  • École Polytechnique Fédérale de Lausanne (CH)
  • Ecole Polytechnique Fédérale de Lausanne (EPFL) , , ,
Venue
ACS Catalysis
Published
2020-05-26
Volume
10
Issue
13
Pages
7021-7031
ISSN
2155-5435 (ISSN-L)
OpenAlex
W3029876348
Cited by
68
References
68
Citations by year
2026: 2 · 2025: 9 · 2024: 14 · 2023: 12 · 2022: 16 · 2021: 9 · 2020: 6
FWCI
4.18
Type
journal article
Language
en
Open access
Locations
  • ACS Catalysis (publishedVersion, cc-by-nc-nd)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Funders
  • École Polytechnique Fédérale de Lausanne (NA)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (200020_175496)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Chemistry › Organic Chemistry › Organometallic Complex Synthesis and Catalysis
  • Physical Sciences › Engineering › Biomedical Engineering › Catalysis for Biomass Conversion
Keywords
machine learning for catalysis, volcano plot, phosphine ligands, N-heterocyclic carbene ligands, lignin degradation, biomass valorization, catalyst activity prediction

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