2023
DOI: 10.1149/1945-7111/aceab2
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Editors’ Choice—AutoEIS: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy

Runze Zhang,
Robert Black,
Debashish Sur
et al.

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 d… Show more

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Cited by 10 publications
(5 citation statements)
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“…Further, following new approaches of HTp equivalent circuit model fitting of EIS data, more mechanisms and interfacial information can be extracted. 50 While the differences in measurement parameters and associated measurement time lead to some variation from conventionally reported values, 51,52 we posit that, for a quick comparison between alloy compositions, the accuracy of our measurements is sufficient to determine critical trends in acidified sulfate environment. For HTp SDC testing approach, the relevant parameters of a high fidelity sequence can be customized for a given set of experimental conditions, including electrolyte, pH, cathodic reduction potential and hold time, flow rates, and droplet dimensions.…”
Section: Discussionmentioning
confidence: 85%
“…Further, following new approaches of HTp equivalent circuit model fitting of EIS data, more mechanisms and interfacial information can be extracted. 50 While the differences in measurement parameters and associated measurement time lead to some variation from conventionally reported values, 51,52 we posit that, for a quick comparison between alloy compositions, the accuracy of our measurements is sufficient to determine critical trends in acidified sulfate environment. For HTp SDC testing approach, the relevant parameters of a high fidelity sequence can be customized for a given set of experimental conditions, including electrolyte, pH, cathodic reduction potential and hold time, flow rates, and droplet dimensions.…”
Section: Discussionmentioning
confidence: 85%
“…To address the growing challenges in processing EIS data, multiple research groups have independently explored the use of supervised machine learning (ML) to automate the identification of appropriate equivalent circuits. [12][13][14][15][16][17] This trend is not exclusive to EIS but is also observed in the broader field of electrochemical sciences, including the analysis of voltammetric data. [18][19][20][21][22] A plethora of methodologies have been spotlighted, including decision trees, [12] random forests, [12,14] the naive Bayes classifier, [12] AdaBoost, [12] support vector machines, [15,16] and various neural network (NN) architectures.…”
Section: Introductionmentioning
confidence: 75%
“…[18][19][20][21][22] A plethora of methodologies have been spotlighted, including decision trees, [12] random forests, [12,14] the naive Bayes classifier, [12] AdaBoost, [12] support vector machines, [15,16] and various neural network (NN) architectures. [13,14,[18][19][20] While these strategies have achieved commendable success -with some classifier algorithms nearing a staggering 99 % accuracy [11,17,19] their drawback remains the indispensable need for labeled data. The term "labeled data" refers to the process of matching specific equivalent circuits with their corresponding EIS spectra.…”
Section: Introductionmentioning
confidence: 99%
“…S2 †). 64,65 As for the normalization, such procedures can require the combination of metadata and experimental data. Parameters of the analysis as well as result data are stored aer quality control of the analysis procedure.…”
Section: Processing and Analysismentioning
confidence: 99%