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EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms
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AI
DOI：10.1016/j.electacta.2022.140350.png)
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摘要
En 中文
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.
Keyword:
Electrochemical impedance spectroscopy
Equivalent circuit model
Interpretable machine learning
Classification
Global optimization algorithm
Parameter identification
期刊
IF:
5.6
论文数:
4.1W
被引数:
12.6W
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暂无机构信息
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