Cargando…
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...
Autores principales: | , |
---|---|
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 |
_version_ | 1783437232737091584 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6644342 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT okamotoyasuharu abinitiocalculationsoftheredoxpotentialsofadditivesforlithiumionbatteriesandtheirpredictionthroughmachinelearning AT kuboyoshimi abinitiocalculationsoftheredoxpotentialsofadditivesforlithiumionbatteriesandtheirpredictionthroughmachinelearning |