Machine learning assisted analysis of equivalent circuit usage in electrochemical impedance spectroscopy applications
© 2024 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 45(2024), 16 vom: 15. Apr., Seite 1380-1389 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2024
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article computer vision electrochemical impedance spectroscopy electrochemistry equivalent circuits machine learning |
| Zusammenfassung: | © 2024 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC. Electrical equivalent circuits are a widely applied tool with which electrical processes can be rationalized. There is a wide-ranging selection of fields from bioelectrochemistry to batteries to fuel cells making use of this tool. Enabling meta-analysis on the similarities and differences in the used circuits will help to identify commonly used circuits and aid in evaluating the underlying physics. We present a method and an implementation that enables the conversion of circuits included in scientific publications into a machine-readable form for generating machine learning datasets or circuit simulations |
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| Beschreibung: | Date Revised 29.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.27334 |