Harnessing evolutionary programming for impedance spectroscopy analysis: A case study of mixed ionic-electronic conductors
Abstract A modified Genetic Programming (GP) method has been developed for the analysis of impedance spectroscopy data. It gives a functional form of the distribution function of relaxation times (DFRT) in the sample. The evolution force is composed of lowering the discrepancy between the model's prediction and the measured data, while keeping the model simple in terms of the number of free parameters. The DFRT that the program seeks for has the form of a peak or a sum of several peaks. All the peaks are known mathematical functions (e.g., Gaussians). The user can let the program search for many types of peaks or to limit the search. Finding a functional form of the underlying DFRT has two main assets. (a) DFRT is unique and (b) a functional form makes it possible to develop a physical model and compare it to the function. In addition, if more than one peak is present and each peak can be related to a different phenomenon, the peaks can be directly separated for further analysis. The analysis method is demonstrated using synthetic data as well as experimental data of Gd 0.1 Ce 0.9 O 1.95 (GDC).