期刊
JOURNAL OF ELECTROANALYTICAL CHEMISTRY
卷 855, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jelechem.2019.113627
关键词
Machine learning; Electrochemical impedance spectroscopy; Equivalent circuit model; Pattern recognition; Support vector machine
资金
- National Natural Science Foundation of China [51801135, 51771136]
- Science and Technology Support Program of Tianjin [16ZXCLGX00110, 16ZXCLGX00070]
- China Postdoctoral Science Foundation [2019M650047]
- Independent Innovation Fund of Tianjin University [2019XZY-0021]
Electrochemical impedance spectroscopy (EIS) is an effective method for studying electrochemical systems. The interpretation of EIS is the biggest challenge in this technology, which requires reasonable modeling. To overcome the subjectivity of human analysis, this work uses machine learning to carry out EIS model recognition. Raw EIS data and their equivalent circuit models are collected from the literature, and the support vector machine (SVM) is used to analyze these data. Comparing with other machine learning algorithms, SVM achieves the best comprehensive performance in this database. As a result, the optimized SVM model can efficiently figure out the most suitable equivalent circuit model of the given EIS spectrum. This study demonstrates the great potential of machine learning in electrochemical researches.
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