EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms
Among seven multiclassification machine learning (ML) models taking optimized hyperparameters found by grid search, AdaBoost achieved the known highest equivalent circuit model (ECM) prediction accuracy, 0.571, and had a prediction basis that was consistent with a common chemical knowledge—the slowe...
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| Published in | Electrochimica acta Vol. 418; p. 140350 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ltd
20.06.2022
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0013-4686 1873-3859 |
| DOI | 10.1016/j.electacta.2022.140350 |
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| Abstract | Among seven multiclassification machine learning (ML) models taking optimized hyperparameters found by grid search, AdaBoost achieved the known highest equivalent circuit model (ECM) prediction accuracy, 0.571, and had a prediction basis that was consistent with a common chemical knowledge—the slowest step usually is vital in the whole electrochemical process. Twenty global optimization algorithms (GOA)s were assessed on simulated and experimental impedance spectra belonging to nine different ECMs, which proved that GOAs obtained nearly the same identification accuracy as the artificial identification under no interference of obvious abnormal points. ML combining with GOA provides a new possibility to automatically process EIS.
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| AbstractList | Among seven multiclassification machine learning (ML) models taking optimized hyperparameters found by grid search, AdaBoost achieved the known highest equivalent circuit model (ECM) prediction accuracy, 0.571, and had a prediction basis that was consistent with a common chemical knowledge-the slowest step usually is vital in the whole electrochemical process. Twenty global optimization algorithms (GOA)s were assessed on simulated and experimental impedance spectra belonging to nine different ECMs, which proved that GOAs obtained nearly the same identification accuracy as the artificial identification under no interference of obvious abnormal points. ML combining with GOA provides a new possibility to automatically process EIS. Among seven multiclassification machine learning (ML) models taking optimized hyperparameters found by grid search, AdaBoost achieved the known highest equivalent circuit model (ECM) prediction accuracy, 0.571, and had a prediction basis that was consistent with a common chemical knowledge—the slowest step usually is vital in the whole electrochemical process. Twenty global optimization algorithms (GOA)s were assessed on simulated and experimental impedance spectra belonging to nine different ECMs, which proved that GOAs obtained nearly the same identification accuracy as the artificial identification under no interference of obvious abnormal points. ML combining with GOA provides a new possibility to automatically process EIS. [Display omitted] |
| ArticleNumber | 140350 |
| Author | Wen, Lei Zou, Yang Liu, Peng Zhao, Zhaoyang Lai, Zhaogui Jin, Ying |
| Author_xml | – sequence: 1 givenname: Zhaoyang surname: Zhao fullname: Zhao, Zhaoyang – sequence: 2 givenname: Yang surname: Zou fullname: Zou, Yang – sequence: 3 givenname: Peng surname: Liu fullname: Liu, Peng – sequence: 4 givenname: Zhaogui surname: Lai fullname: Lai, Zhaogui – sequence: 5 givenname: Lei surname: Wen fullname: Wen, Lei email: wenlei@ustb.edu.cn – sequence: 6 givenname: Ying surname: Jin fullname: Jin, Ying email: yjin@ustb.edu.cn |
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| Keywords | Interpretable machine learning Equivalent circuit model Parameter identification Classification Global optimization algorithm Electrochemical impedance spectroscopy |
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