Equivalent Circuit Model Recognition of Electrochemical Impedance Spectroscopy via Machine Learning

TL;DR

This paper applies machine learning, specifically support vector machines, to classify electrochemical impedance spectroscopy data and recognize equivalent circuit models, achieving up to 78% accuracy, thus reducing subjective interpretation.

Contribution

It introduces a machine learning approach for EIS pattern recognition, addressing the subjectivity in traditional modeling methods.

Findings

Achieved up to 78% classification accuracy.

Demonstrated potential of machine learning in electrochemical analysis.

Collected diverse EIS data from literature for training.

Abstract

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.