Analysis of High-Throughput Electrochemical Impedance Spectroscopy via Hierarchical Bayesian Models

Abstract

Electrochemical impedance spectroscopy (EIS) is a powerful characterization technique that enables in-situ investigation of electrochemical processes over a wide range of timescales, making it particularly effective in analysis of electrochemical energy conversion devices such as fuel cells and batteries. The advent of automated high-throughput EIS systems presents an exciting opportunity for materials discovery, rapidly providing detailed information about candidate materials. However, the utility of EIS as an electrochemical method is paralleled by the complexity of interpreting the resulting data: overlapping features, noise, and measurement artifacts make EIS spectra notoriously difficult to analyze. Equivalent circuit models (ECMs) are widely used to deconvolute EIS data, but there is significant ambiguity in model selection; meanwhile, the distribution of relaxation times (DRT) has become increasingly popular as an alternative/complementary deconvolution method, but faces similar uncertainty in the choice of regularization parameters. The sheer volume of data produced by high-throughput methods amplifies these challenges, necessitating automated, self-consistent analysis procedures.Here, we discuss high-throughput EIS characterization of protonic ceramic electrode thin films and demonstrate the application of hierarchical Bayesian models for robust analysis of the data. The calibrated hierarchical Bayesian DRT resolves much of the ambiguity in regularization parameters by encoding desired properties of the DRT in prior distributions [1]. Meanwhile, the versatility of the DRT enables appropriate deconvolution of thousands of spectra without predetermined model selection. The DRT may be translated to an ECM for further quantitative analysis of individual relaxation timescales and magnitudes by leveraging a flexible semi-empirical model. In addition, advanced Bayesian DRT models can be implemented to address outliers, sample drift, and multi-spectrum deconvolution. These tools enable conversion of raw spectra to a dataset amenable to further interpretation via machine learning and other methods, thereby expanding opportunities to distill meaningful insight from high-throughput electrochemical studies.