Editors’ Choice—AutoEIS: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy

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 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.

A High Throughput Aqueous Passivation Testing Methodology for Compositionally Complex Alloys Using a Scanning Droplet Cell

“…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.…”

Is Unsupervised Dimensionality Reduction Sufficient to Decode the Complexities of Electrochemical Impedance Spectra?

“…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.…”

“…[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.…”

Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory

“…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.…”