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Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers

  • Polymers have become indispensable across fields of application, and understanding their structure–property relationships and dynamic behaviour is essential for performance optimization. Polymer membranes, particularly ion exchange membranes, play a crucial role in renewable energy conversion technologies, fuel cells, solar energy conversion, and energy storage. In this context, broadband dielectric spectroscopy (BDS) offers a powerful, non-destructive approach to investigate the electrical response and relaxation dynamics of polymers. These properties are investigated by parametrizing the system’s impedance response in terms of a network of circuit elements, i.e. the electrical equivalent circuit (EEC), whose impedance resembles the one of the system under investigation. However, the determination of the EEC from BDS data is challenging due to system complexity, interdependencies of circuit elements, and researcher biases. In this work, we propose a novel approach that incorporates aPolymers have become indispensable across fields of application, and understanding their structure–property relationships and dynamic behaviour is essential for performance optimization. Polymer membranes, particularly ion exchange membranes, play a crucial role in renewable energy conversion technologies, fuel cells, solar energy conversion, and energy storage. In this context, broadband dielectric spectroscopy (BDS) offers a powerful, non-destructive approach to investigate the electrical response and relaxation dynamics of polymers. These properties are investigated by parametrizing the system’s impedance response in terms of a network of circuit elements, i.e. the electrical equivalent circuit (EEC), whose impedance resembles the one of the system under investigation. However, the determination of the EEC from BDS data is challenging due to system complexity, interdependencies of circuit elements, and researcher biases. In this work, we propose a novel approach that incorporates a convolutional neural network (CNN) model to predict the EEC topology. By reducing user bias and enhancing data analysis, this approach aims to make BDS accessible to both experienced users and those with limited expertise. We show that the combination of machine learning and BDS provides valuable insights into the dynamic behaviour of polymer membranes, thus facilitating the design and characterization of tailored polymers for various applications. We also show that our model outperforms state-of-the-art machine learning methods with a top-5 accuracy of around 80% for predicting the circuit topology and a parameter fitting error as low as 0.05%.zeige mehrzeige weniger

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Metadaten
Autor*innen:Bashar Albakri, Analice Turski Silva Diniz, Philipp Benner, Thilo Muth, Shinichi Nakajima, Marco Favaro, Alexander Kister
Persönliche Herausgeber*innen:Robert Hillman
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Electrochimica Acta
Jahr der Erstveröffentlichung:2024
Organisationseinheit der BAM:VP Vizepräsident
VP Vizepräsident / VP.1 eScience
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Herausgeber (Institution):International Society of Electrochemistry (ISE)
Verlag:Elsevier Ltd.
Jahrgang/Band:496
Aufsatznummer:144474
Erste Seite:1
Letzte Seite:13
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik
Freie Schlagwörter:Broadband dielectric spectroscopy; Deep learning; Electrochemical impedance spectroscopy; Equivalent circuit; Machine learning; Polymer membranes
Themenfelder/Aktivitätsfelder der BAM:Energie
Energie / Elektrische Energiespeicher und -umwandlung
DOI:10.1016/j.electacta.2024.144474
URN:urn:nbn:de:kobv:b43-602138
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:10.06.2024
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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