Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks

Abstract

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.