  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.
