AutoEIS: automated Bayesian model selection and analysis for electrochemical impedance spectroscopy

TL;DR

AutoEIS is an open-source tool that automates the interpretation of electrochemical impedance spectroscopy data by proposing statistically supported equivalent circuit models across diverse systems, reducing user bias and expert knowledge requirements.

Contribution

AutoEIS introduces a generalized automated Bayesian approach for EIS model selection, enabling high-throughput analysis without expert labels or mechanistic understanding.

Findings

AutoEIS successfully analyzed three distinct electrochemical systems.

It identified superior or competitive models compared to expert recommendations.

AutoEIS reduced user bias and facilitated high-throughput EIS analysis.

Abstract

Electrochemical Impedance Spectroscopy (EIS) is a powerful tool for electrochemical analysis; however, its data can be challenging to interpret. Here, we introduce a new open-source tool named AutoEIS that assists EIS analysis by automatically proposing statistically plausible equivalent circuit models (ECMs). AutoEIS does this without requiring an exhaustive mechanistic understanding of the electrochemical systems. We demonstrate the generalizability of AutoEIS by using it to analyze EIS datasets from three distinct electrochemical systems, including thin-film oxygen evolution reaction (OER) electrocatalysis, corrosion of self-healing multi-principal components alloys, and a carbon dioxide reduction electrolyzer device. In each case, AutoEIS identified competitive or in some cases superior ECMs to those recommended by experts and provided statistical indicators of the preferred solution. The results demonstrated AutoEIS's capability to facilitate EIS analysis without expert labels while diminishing user bias in a high-throughput manner. AutoEIS provides a generalized automated approach to facilitate EIS analysis spanning a broad suite of electrochemical applications with minimal prior knowledge of the system required. This tool holds great potential in improving the efficiency, accuracy, and ease of EIS analysis and thus creates an avenue to the widespread use of EIS in accelerating the development of new electrochemical materials and devices.