Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks
Technology advancements in energy storage, photocatalysis, and sensors have generated enormous impedimetric data. Electrochemical impedance spectroscopy (EIS) results play an essential role in analyzing the interfacial properties of materials. Nonetheless, in many situations, the data is misinterpre...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
|
| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202300085 |
| _version_ | 1797739591531233280 |
|---|---|
| author | Dulyawat Doonyapisut Padmanathan-Karthick Kannan Byeongkyu Kim Jung Kyu Kim Eunseok Lee Chan-Hwa Chung |
| author_facet | Dulyawat Doonyapisut Padmanathan-Karthick Kannan Byeongkyu Kim Jung Kyu Kim Eunseok Lee Chan-Hwa Chung |
| author_sort | Dulyawat Doonyapisut |
| collection | DOAJ |
| description | Technology advancements in energy storage, photocatalysis, and sensors have generated enormous impedimetric data. Electrochemical impedance spectroscopy (EIS) results play an essential role in analyzing the interfacial properties of materials. Nonetheless, in many situations, the data is misinterpreted due to the complexity of the electrochemical system or the compromise between the experimental result and the theoretical model, resulting in partiality in the interpretation process, especially for the impedimetric results. Typically, the experimenter interprets impedimetric results using a searching approach based on a theoretical model until the best‐fitting model is obtained, which is a time‐consuming process, and errors can occur. To reduce misinterpretation by the experimenter, herein, the machine‐learning strategy is demonstrated for the classification of an EIS circuit model and parameter prediction using a deep neural network (DNN). The DNN model shows a highly accurate classifier for the commonly used EIS circuit with an average area under the receiver operating characteristic curve of more than 0.95. Additionally, the model demonstrates high accuracy in the prediction of EIS parameters on a complex EIS system, with a maximum R2 of 0.999. These reveal that the machine‐learning strategy may open a new room for studying electrochemical systems. |
| first_indexed | 2024-03-12T14:00:24Z |
| format | Article |
| id | doaj.art-086454d655f448e88291f114f7eb094f |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-03-12T14:00:24Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-086454d655f448e88291f114f7eb094f2023-08-22T05:33:15ZengWileyAdvanced Intelligent Systems2640-45672023-08-0158n/an/a10.1002/aisy.202300085Analysis of Electrochemical Impedance Data: Use of Deep Neural NetworksDulyawat Doonyapisut0Padmanathan-Karthick Kannan1Byeongkyu Kim2Jung Kyu Kim3Eunseok Lee4Chan-Hwa Chung5School of Chemical Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaDepartment of Chemistry Sungkyunkwan University Suwon 16419 Republic of KoreaSchool of Chemical Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaSchool of Chemical Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaCollege of Computing and Informatics Sungkyunkwan University Suwon 16419 Republic of KoreaSchool of Chemical Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaTechnology advancements in energy storage, photocatalysis, and sensors have generated enormous impedimetric data. Electrochemical impedance spectroscopy (EIS) results play an essential role in analyzing the interfacial properties of materials. Nonetheless, in many situations, the data is misinterpreted due to the complexity of the electrochemical system or the compromise between the experimental result and the theoretical model, resulting in partiality in the interpretation process, especially for the impedimetric results. Typically, the experimenter interprets impedimetric results using a searching approach based on a theoretical model until the best‐fitting model is obtained, which is a time‐consuming process, and errors can occur. To reduce misinterpretation by the experimenter, herein, the machine‐learning strategy is demonstrated for the classification of an EIS circuit model and parameter prediction using a deep neural network (DNN). The DNN model shows a highly accurate classifier for the commonly used EIS circuit with an average area under the receiver operating characteristic curve of more than 0.95. Additionally, the model demonstrates high accuracy in the prediction of EIS parameters on a complex EIS system, with a maximum R2 of 0.999. These reveal that the machine‐learning strategy may open a new room for studying electrochemical systems.https://doi.org/10.1002/aisy.202300085deep learningEIS analysisEIS predictionimpedance machine learning |
| spellingShingle | Dulyawat Doonyapisut Padmanathan-Karthick Kannan Byeongkyu Kim Jung Kyu Kim Eunseok Lee Chan-Hwa Chung Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks Advanced Intelligent Systems deep learning EIS analysis EIS prediction impedance machine learning |
| title | Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks |
| title_full | Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks |
| title_fullStr | Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks |
| title_full_unstemmed | Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks |
| title_short | Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks |
| title_sort | analysis of electrochemical impedance data use of deep neural networks |
| topic | deep learning EIS analysis EIS prediction impedance machine learning |
| url | https://doi.org/10.1002/aisy.202300085 |
| work_keys_str_mv | AT dulyawatdoonyapisut analysisofelectrochemicalimpedancedatauseofdeepneuralnetworks AT padmanathankarthickkannan analysisofelectrochemicalimpedancedatauseofdeepneuralnetworks AT byeongkyukim analysisofelectrochemicalimpedancedatauseofdeepneuralnetworks AT jungkyukim analysisofelectrochemicalimpedancedatauseofdeepneuralnetworks AT eunseoklee analysisofelectrochemicalimpedancedatauseofdeepneuralnetworks AT chanhwachung analysisofelectrochemicalimpedancedatauseofdeepneuralnetworks |