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240627s2024 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.27380
|2 doi
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|a DE-627
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|a eng
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1 |
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|a Jia, Linlin
|e verfasserin
|4 aut
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|a Predicting redox potentials by graph-based machine learning methods
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 02.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.
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|a The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource-intensive and time-consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph-based methods (such as graph edit distances, graph kernels, and graph neural networks) that are reviewed to enlighten their deep links with theoretical chemistry. To this aim, we establish the ORedOx159 database, a comprehensive, homogeneous (with reference values stemming from density functional theory calculations), and reliable resource containing 318 one-electron reduction and oxidation reactions and featuring 159 large organic compounds. Subsequently, we provide an instructive overview of the good practice in machine learning and of commonly utilized machine learning models. We then assess their predictive performances on the ORedOx159 dataset through extensive analyses. Our simulations using descriptors that are computed in an almost instantaneous way result in a notable improvement in prediction accuracy, with mean absolute error (MAE) values equal to 5.6 kcal mol - 1 for reduction and 7.2 kcal mol - 1 for oxidation potentials, which paves a way toward efficient in silico design of new electrochemical systems
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|a Journal Article
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|a ORedOx159 database
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4 |
|a Redox potential prediction
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4 |
|a density functional theory
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| 650 |
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|a graph‐based machine learning methods
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| 700 |
1 |
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|a Brémond, Éric
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zaida, Larissa
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gaüzère, Benoit
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tognetti, Vincent
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Joubert, Laurent
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 45(2024), 28 vom: 30. Okt., Seite 2383-2396
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnas
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| 773 |
1 |
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|g volume:45
|g year:2024
|g number:28
|g day:30
|g month:10
|g pages:2383-2396
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|u http://dx.doi.org/10.1002/jcc.27380
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