Serialization of Electrochemical Impedance Spectra to Forecast Low-Frequency Impedances via Deep Learning Neural Networks

Electrochemical impedance spectroscopy offers valuable insights into interfacial properties but acquiring low-frequency (LF) data can be time-consuming. This work explores time series analysis for forecasting LF impedance based on readily available high-frequency (HF) data. The time series methods like long-short term memory, convolutional neural network, and transformer were studied. Suitable neural network (NN) structure was proposed. The impact of forecasting window size was investigated to determine how much information was necessary for NN to establish useful connection. This approach holds promise for faster, more efficient, and insightful analysis of electrochemical systems.