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

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Runze Zhang , Robert W. Black , Debashish Sur , Parisa Karimi , Kangming Li , Brian DeCost , John R. Scully , Jason Hattrick‐Simpers ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2305.04841

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.

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Dielectric spectroscopy Computer science Generalizability theory Electrochemistry Electrode Chemistry Physical chemistry Statistics Mathematics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2305.04841
PDF: https://arxiv.org/pdf/2305.04841
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4375959490

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DOI: https://doi.org/10.48550/arxiv.2305.04841

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

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-05-08

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Authors: Runze Zhang, Robert W. Black, Debashish Sur, Parisa Karimi, Kangming Li, Brian DeCost, John R. Scully, Jason Hattrick‐Simpers

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Landing page: https://arxiv.org/abs/2305.04841

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PDF URL: https://arxiv.org/pdf/2305.04841

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Concepts: Dielectric spectroscopy, Computer science, Generalizability theory, Electrochemistry, Electrode, Chemistry, Physical chemistry, Statistics, Mathematics

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Cited by: 1

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