  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.
