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

[EN] 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 th...

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Autores: Sáez-Pardo, Fermín|||0009-0000-0759-6781, Giner-Sanz, Juan José|||0000-0003-0441-6102, Pérez-Herranz, Valentín|||0000-0002-4010-0888
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/231703
Acceso en línea:https://riunet.upv.es/handle/10251/231703
Access Level:acceso abierto
Palabra clave:Artificial vision
Convolutional neural networks
Deep learning
Electrical equivalent circuits
Electrochemical impedance spectroscopy
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oai_identifier_str oai:riunet.upv.es:10251/231703
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
title Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
spellingShingle Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
Sáez-Pardo, Fermín|||0009-0000-0759-6781
Artificial vision
Convolutional neural networks
Deep learning
Electrical equivalent circuits
Electrochemical impedance spectroscopy
title_short Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
title_full Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
title_fullStr Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
title_full_unstemmed Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
title_sort Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy
dc.creator.none.fl_str_mv Sáez-Pardo, Fermín|||0009-0000-0759-6781
Giner-Sanz, Juan José|||0000-0003-0441-6102
Pérez-Herranz, Valentín|||0000-0002-4010-0888
author Sáez-Pardo, Fermín|||0009-0000-0759-6781
author_facet Sáez-Pardo, Fermín|||0009-0000-0759-6781
Giner-Sanz, Juan José|||0000-0003-0441-6102
Pérez-Herranz, Valentín|||0000-0002-4010-0888
author_role author
author2 Giner-Sanz, Juan José|||0000-0003-0441-6102
Pérez-Herranz, Valentín|||0000-0002-4010-0888
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Química y Nuclear
Escuela Técnica Superior de Ingeniería Industrial
Instituto Universitario de Seguridad Industrial, Radiofísica y Medioambiental
Ministerio de Educación
AGENCIA ESTATAL DE INVESTIGACION
European Regional Development Fund
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Artificial vision
Convolutional neural networks
Deep learning
Electrical equivalent circuits
Electrochemical impedance spectroscopy
topic Artificial vision
Convolutional neural networks
Deep learning
Electrical equivalent circuits
Electrochemical impedance spectroscopy
description [EN] 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/231703
url https://riunet.upv.es/handle/10251/231703
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Universitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-06-23 Optimización del anolito de una batería de todo hierro (OptAnol-FeBat)
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 PID2023-147511OB-C21 Caracterización electroquímica de electrodos cerámicos y aplicación al desarrollo de baterías de flujo redox y a la eliminación de contaminantes emergentes
Universitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-01-22 Espectroscopía de impedancias electroquímicas. Identificación de circuitos eléctricos equivalentes vía inteligencia artificial.
MINISTERIO DE EDUCACION MINISTERIO DE EDUCACION IJC2020-044087-I Electrochemistry: from fundamentals to energy and environmental applications
Universitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-12-25 Ayudas a investigadores predoctorales para la publicación de artículos de investigación en abierto
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance SpectroscopySáez-Pardo, Fermín|||0009-0000-0759-6781Giner-Sanz, Juan José|||0000-0003-0441-6102Pérez-Herranz, Valentín|||0000-0002-4010-0888Artificial visionConvolutional neural networksDeep learningElectrical equivalent circuitsElectrochemical impedance spectroscopy[EN] 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.This work was funded by an "Ayuda a Primeros Proyectos de Investigacion" project (PAID-06-23) of the research vice-rectorate of Universitat Politecnica de Valencia and the Grant PID2023-147511OB-C21 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. F.S.P. acknowledges the support of Universitat Politecnica de Valencia through a predoctoral fellowship (PAID-01-22) . J.J.G.S. is very grateful to the Ministerio de Ciencia e Innovacion, to the Next Generation EU, and to the Agencia Estatal de Investigacion, for their support by a Juan de la Cierva-Incorporacion fellowship (IJC2020-044087-I) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. Authors thank Universitat Politecnica de Valencia for funding for open access charge (PAID-12-25) .Elsevier BVDepartamento de Ingeniería Química y NuclearEscuela Técnica Superior de Ingeniería IndustrialInstituto Universitario de Seguridad Industrial, Radiofísica y MedioambientalMinisterio de EducaciónAGENCIA ESTATAL DE INVESTIGACIONEuropean Regional Development FundUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/231703reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUniversitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-06-23 Optimización del anolito de una batería de todo hierro (OptAnol-FeBat)Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 PID2023-147511OB-C21 Caracterización electroquímica de electrodos cerámicos y aplicación al desarrollo de baterías de flujo redox y a la eliminación de contaminantes emergentesUniversitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-01-22 Espectroscopía de impedancias electroquímicas. Identificación de circuitos eléctricos equivalentes vía inteligencia artificial.MINISTERIO DE EDUCACION MINISTERIO DE EDUCACION IJC2020-044087-I Electrochemistry: from fundamentals to energy and environmental applicationsUniversitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-12-25 Ayudas a investigadores predoctorales para la publicación de artículos de investigación en abiertoopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2317032026-06-13T07:49:27Z
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