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Machine Learning-Assisted Equivalent Circuit Identification for Dielectric Spectroscopy of Polymers

Bashar Albakri, Analice Turski Silva Diniz,Philipp Benner,Thilo Muth,Shinichi Nakajima,Marco Favaro,Alexander Kister

ELECTROCHIMICA ACTA(2024)

BAM Fed Inst Mat Res & Testing

Cited 0|Views7
Abstract
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%.
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Key words
Polymer membranes,Electrochemical impedance spectroscopy,Broadband dielectric spectroscopy,Deep learning,Machine learning,Equivalent circuit
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要点】:本文提出了一种结合卷积神经网络(CNN)的机器学习辅助方法,用于识别聚合物宽带介电光谱的等效电路,提高了参数拟合的准确性和效率,降低了用户偏见。

方法】:作者利用CNN模型预测等效电路(EEC)的拓扑结构,通过训练模型来减少系统复杂性、电路元素相互依赖性以及研究者偏见带来的影响。

实验】:实验中使用宽带介电光谱(BDS)数据集,证明了所提出的CNN模型在预测电路拓扑方面具有大约80%的top-5准确率,以及参数拟合误差低至0.05%。