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Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning

[Image: see text] Ab initio molecular orbital calculations were carried out to examine the redox potentials of 149 electrolyte additives for lithium-ion batteries. These potentials were employed to construct regression models using a machine learning approach. We chose simple descriptors based on th...

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Autores principales: Okamoto, Yasuharu, Kubo, Yoshimi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2018
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644342/
https://www.ncbi.nlm.nih.gov/pubmed/31458929
http://dx.doi.org/10.1021/acsomega.8b00576
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author Okamoto, Yasuharu
Kubo, Yoshimi
author_facet Okamoto, Yasuharu
Kubo, Yoshimi
author_sort Okamoto, Yasuharu
collection PubMed
description [Image: see text] Ab initio molecular orbital calculations were carried out to examine the redox potentials of 149 electrolyte additives for lithium-ion batteries. These potentials were employed to construct regression models using a machine learning approach. We chose simple descriptors based on the chemical structure of the additive molecules. The descriptors predicted the redox potentials well, in particular, the oxidation potentials. We found that the redox potentials can be explained by a small number of features, which improve the interpretability of the results and seem to be related to the amplitude of the eigenstate of the frontier orbitals.
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spelling pubmed-66443422019-08-27 Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning Okamoto, Yasuharu Kubo, Yoshimi ACS Omega [Image: see text] Ab initio molecular orbital calculations were carried out to examine the redox potentials of 149 electrolyte additives for lithium-ion batteries. These potentials were employed to construct regression models using a machine learning approach. We chose simple descriptors based on the chemical structure of the additive molecules. The descriptors predicted the redox potentials well, in particular, the oxidation potentials. We found that the redox potentials can be explained by a small number of features, which improve the interpretability of the results and seem to be related to the amplitude of the eigenstate of the frontier orbitals. American Chemical Society 2018-07-13 /pmc/articles/PMC6644342/ /pubmed/31458929 http://dx.doi.org/10.1021/acsomega.8b00576 Text en Copyright © 2018 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Okamoto, Yasuharu
Kubo, Yoshimi
Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning
title Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning
title_full Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning
title_fullStr Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning
title_full_unstemmed Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning
title_short Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning
title_sort ab initio calculations of the redox potentials of additives for lithium-ion batteries and their prediction through machine learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644342/
https://www.ncbi.nlm.nih.gov/pubmed/31458929
http://dx.doi.org/10.1021/acsomega.8b00576
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