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Researchers study the properties of polymeric membranes - an important building block of batteries – by first measuring its impedance spectra and then determining a so called equivalent electronic circuit (EEC), that is an electronic circuit that approximately reproduces these measurements. Determining EEC is not completely automatized since it includes the search for the topology of the electronic circuit. This search is an inverse problem, since the formulas for determining the impedance spectra given the circuit topology and parameters are known while there are no formulas for the invers direction. To solve this invers problem, the original problem is split into two parts. The first part is to determine the topology of the circuit and the second part is to determine the parameters of the circuit. For the first part a Convolutional Neural Network Classifier is trained on a simulated data set, where the simulator is an implementation of the formulas for determining the impedance spectra given the complete description of the electronic circuit. After the topology is determined, the parameters of the electronic circuit are found by minimizing the error between the observed spectra and the spectra corresponding to these parameters. This minimization is a global optimization.
Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers
(2024)
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%.