Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy

•A new Deep Learning algorithm was developed for EEC recommendation for EIS spectra•Convolutional Neural Networks were optimized for the first time for EEC recommendation•The algorithm achieved better recommendation accuracies than previous AI algorithms•The algorithm was applied to an experimental...

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Bibliographic Details
Published in:Systems and soft computing p. 200423
Main Authors: Sáez-Pardo, Fermín, Giner-Sanz, Juan José, Pérez-Herranz, Valentín
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2025
Subjects:
Artificial Vision
Convolutional Neural Networks
Deep Learning
Electrical Equivalent Circuits
Electrochemical Impedance Spectroscopy
Artificial Vision
Electrochemical Impedance Spectroscopy
Convolutional Neural Networks
Deep Learning
Electrical Equivalent Circuits
ISSN:2772-9419, 2772-9419
Online Access:Get full text
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Summary:•A new Deep Learning algorithm was developed for EEC recommendation for EIS spectra•Convolutional Neural Networks were optimized for the first time for EEC recommendation•The algorithm achieved better recommendation accuracies than previous AI algorithms•The algorithm was applied to an experimental EIS of a commercial Li-V2O5 battery•Though the results are far from perfect, CNNs seem promising for EEC recommendation Electrochemical Impedance Spectroscopy (EIS) is a technique widely used in the field of electrochemistry, due to its ability to probe the dynamics of electrochemical systems. Commonly, EIS spectra are analysed using Equivalent Electrical Circuits (EECs). The EEC selection is not trivial. For this reason, Digby D. Macdonald proposed the Electrochemical Genome Project, which would consist of a large database of EIS spectra and an Artificial Intelligence able to recommend EECs given an experimental EIS spectrum. In this work, we developed a Deep Learning algorithm, based on Convolutional Neural Networks (CNNs), for EEC recommendation for EIS spectra. To achieve this, we optimized the CNN in 3 sequential stages: first, the convolutional architecture was optimized; then, the Initial Learn Rate was selected; and finally, the dense network architecture was optimized. At the end of this process, we obtained a CNN model with a maximum test accuracy of 61.11%. The obtained results show that CNNs are good candidates for EEC recommendation tools for EIS spectra.
ISSN:2772-9419
2772-9419
DOI:10.1016/j.sasc.2025.200423