Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage
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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.
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Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction
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SPOCK Tool for Constructing Empirical Volcano Diagrams from Catalytic Data
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Self-citationBury, G., & Pushkar, Y. (2022) · Catalysts · doi:10.3390/catal12080863 · Cited by 6 · see citing works
van Gerwen, P., Wodrich, M. D., Laplaza, R., & Corminbœuf, C. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acee43 · Cited by 5 · see citing works
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Toward AI/ML-assisted discovery of transition metal complexes
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Large-scale comparison of Fe and Ru polyolefin C–H activation catalysts
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Machine learning-based yield prediction for transition metal-catalyzed cross-coupling reactions
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Methoxycyclization of 1,5‐Enynes by Coinage Metal Catalysts: Is Gold Always Superior?
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Large-scale Comparison of Fe and Ru Polyolefin C–H Activation CatalystsPreprint
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Predicting Reactivity with Machine Learning
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Self-citationReaction Performance Prediction and Reaction Condition Optimization
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Data-driven strategies for decoding structure–reactivity relationships in porous electrocatalysis
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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
- Publisher
- American Chemical Society (ACS)
- 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
- hybrid
- doi.org
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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