基于机器学习的电化学阻抗谱等效电路模型识别
Equivalent Circuit Model Recognition of Electrochemical Impedance Spectroscopy via Machine Learning
摘要：
电化学阻抗谱（EIS）是研究电化学体系的有效方法. EIS的解释是该技术的最大挑战，这需要合理的建模。然而，EIS的建模具有很大的主观性，这意味着可能有几个模型来拟合同一组数据。为了克服人类分析的不确定性和琐碎性，本研究利用机器学习进行EIS模式识别。从文献中收集原始EIS数据及其等效电路模型，并使用支持向量机（SVM）对这些数据进行分析。其结果是，我们解决了EIS的分类和识别其等效电路模型的准确度高达78%。这项研究表明了机器学习在电化学研究中的巨大潜力。
英文摘要：
Electrochemical impedance spectroscopy (EIS) is an effective method for studying the electrochemical systems. The interpretation of EIS is the biggest challenge in this technology, which requires reasonable modeling. However, the modeling of EIS is of great subjectivity, meaning that there may be several models to fit the same set of data. In order to overcome the uncertainty and triviality of human analysis, this research uses machine learning to carry out EIS pattern recognition. Raw EIS data and their equivalent circuit models were collected from the literature, and the support vector machine (SVM) was used to analyze these data. As the result, we addresses the classification of EIS and recognizing their equivalent circuit models with accuracies of up to 78%. This study demonstrates the great potential of machine learning in electrochemical researches.
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