TY - JOUR A1 - Albakri, Bashar A1 - Turski Silva Diniz, Analice A1 - Benner, Philipp A1 - Muth, Thilo A1 - Nakajima, Shinichi A1 - Favaro, Marco A1 - Kister, Alexander ED - Hillman, Robert T1 - Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers JF - Electrochimica Acta N2 - 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%. KW - Polymer membranes KW - Electrochemical impedance spectroscopy KW - Broadband dielectric spectroscopy KW - Deep learning KW - Machine learning KW - Equivalent circuit PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602138 DO - https://doi.org/10.1016/j.electacta.2024.144474 VL - 496 SP - 1 EP - 13 PB - Elsevier Ltd. AN - OPUS4-60213 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -