[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123195-en":3,"doc-seo-123195-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123195,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers","Polymers are essential across energy and materials applications, where linking structure to properties and capturing dynamic behavior is key to performance. Polymer membranes, especially ion exchange membranes, rely on non-destructive characterization to understand electrical response and relaxation. Broadband dielectric spectroscopy (BDS) models these behaviors via electrical equivalent circuits (EECs), yet extracting topology and parameters from BDS data is hindered by complexity, element interdependence, and user bias. The study introduces a convolutional neural network to predict EEC topology, reducing bias and enabling analysis for varied expertise, achieving top-5 accuracy near 80% and fitting error as low as 0.05%.","Electrochimica Acta 496 (2024) 144474  \n| Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers\u003Cbr>Bashar Albakri a, Analice Turski Silva Diniz b, Philipp Benner a, Thilo Muth a, Shinichi Nakajima c,d,e, Marco Favarob, Alexander Kister a,∗\u003Cbr>a Section VP. 1 eScience, Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205, Berlin, Germany b Institute for Solar Fuels, Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Hahn-Meitner-Platz 1, 14109, Berlin, Germany\u003Cbr>c Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany d Berlin Institute for the Foundations of Learning and Data, 10587, Berlin, Germany e RIKEN Center for AIP, Tokyo 103-0027, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O | A B S T R A C T\u003Cbr>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 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%. |  |\n| Dataset link: [https://github.com/BAMeScience](https://github.com/BAMeScience)[ ](https://github.com/BAMeScience)[/EISNet](/EISNet) |  |  |\n| Keywords:\u003Cbr>Polymer membranes\u003Cbr>Electrochemical impedance spectroscopy Broadband dielectric spectroscopy Deep learning\u003Cbr>Machine learning\u003Cbr>Equivalent circuit |  |  |\n\n1. Introduction  \nPolymers, with their diverse applications ranging from materials science to engineering and biotechnology, have become indispensable in our modern world. Understanding the structure–property relationships and dynamic behaviour of polymers is crucial for tailoring their properties and optimizing their performance. Among the many different polymer-based materials, polymer membranes have emerged as key components in various renewable energy conversion technologies, since they play a crucial role in facilitating the efficient generation, storage, and utilization of renewable energy, thus supporting new avenues fora clean and sustainable future. Polymer membranes, commonly known as ion exchange membranes (IEMs), are at the heart of fuel cells. These devices convert the chemical energy of a fuel, such as hydrogen or methanol, into electrical energy through an electrochemical reaction. IEMs enable the selective transport of protons while blocking the  \npassage of electrons, facilitating the controlled flow of ions necessary for fuel cell operation. The high ion conductivity, excellent chemical stability, and mechanical flexibility of polymer membranes make","cbCaifDp7CdIR927","https://ap.wps.com/l/cbCaifDp7CdIR927","pdf",2140718,1,13,"English","en",105,"# Introduction\n## Polymer structure-property relationships and dynamic behavior\n## Role of polymer membranes in renewable energy technologies\n## Broadband dielectric spectroscopy and equivalent circuit modeling\n## Challenge of extracting EEC from BDS data\n## Proposed machine learning approach","[{\"question\":\"为什么需要等效电路（EEC）来分析聚合物的介电光谱数据？\",\"answer\":\"BDS用于研究聚合物的电响应与弛豫动力学，但需要通过电学等效电路对阻抗响应进行参数化，以获得与样品行为相对应的模型。\"},{\"question\":\"从BDS数据确定EEC面临哪些主要困难？\",\"answer\":\"主要困难包括系统复杂度、电路元件之间的相互依赖，以及研究者在分析过程中的偏差，从而影响拓扑确定与参数拟合。\"},{\"question\":\"该文提出的CNN方法如何改善EEC识别？\",\"answer\":\"方法使用卷积神经网络预测EEC拓扑，旨在降低人为偏差并提升数据分析一致性，使具备不同经验水平的用户也能进行BDS分析。\"}]","Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers | PDF",1785815144,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-assisted-equivalent-circuit-identification-for-dielectric-spectroscopy-of-polymers","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-assisted-equivalent-circuit-identification-for-dielectric-spectroscopy-of-polymers/123195/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么需要等效电路（EEC）来分析聚合物的介电光谱数据？","Question",{"text":75,"@type":76},"BDS用于研究聚合物的电响应与弛豫动力学，但需要通过电学等效电路对阻抗响应进行参数化，以获得与样品行为相对应的模型。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"从BDS数据确定EEC面临哪些主要困难？",{"text":80,"@type":76},"主要困难包括系统复杂度、电路元件之间的相互依赖，以及研究者在分析过程中的偏差，从而影响拓扑确定与参数拟合。",{"name":82,"@type":73,"acceptedAnswer":83},"该文提出的CNN方法如何改善EEC识别？",{"text":84,"@type":76},"方法使用卷积神经网络预测EEC拓扑，旨在降低人为偏差并提升数据分析一致性，使具备不同经验水平的用户也能进行BDS分析。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]