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|a 10.1002/jcc.26953
|2 doi
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|a pubmed24n1142.xml
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|a eng
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|a Kanamaru, Yuki
|e verfasserin
|4 aut
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|a Factor analysis of error in oxidation potential calculation
|b A machine learning study
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 19.07.2022
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|a Date Revised 07.09.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2022 Wiley Periodicals LLC.
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|a The conductor-like polarizable continuum model (C-PCM), which is a low-cost solvation model, cannot treat characteristic interactions between the solvent and substructure(s) of the solute. Moreover, the error in a charged system is significant. Using machine learning, we clarified that the systematic error of the oxidation potential calculated by the G3B3/C-PCM was correlated with the molecular size of a solute. The G3B3/C-PCM overestimated the Gibbs oxidation energy by averaging 6.94 kcal/mol. According to the performance of related methods reported in previous studies, this error is mainly due to the solvation energy of the charged solute. Additionally, we succeeded in reducing the error to 2.27 kcal/mol (32%)-3.2 kcal/mol (40%) by correction based on the substructure information of the solute. To modify the C-PCM, effects that correlate with the molecular size of the solute in the charged system should be incorporated
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a B3LYP
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|a C-PCM
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|a G3B3
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|a machine learning
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|a redox potential
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|a Solutions
|2 NLM
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|a Solvents
|2 NLM
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|a Matsui, Toru
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 43(2022), 22 vom: 15. Aug., Seite 1504-1512
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:43
|g year:2022
|g number:22
|g day:15
|g month:08
|g pages:1504-1512
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|u http://dx.doi.org/10.1002/jcc.26953
|3 Volltext
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