Gaussian processes for the analysis of electrochemical impedance spectroscopy data: Prediction, filtering, and active learning

Electrochemical impedance spectroscopy (EIS) is a widespread characterization technique used to study elec-trochemical systems. However, several shortcomings still limit the application of this technique. First, EIS data, unless acquired in well-controlled experiments, is intrinsically noisy, hindering spectra regression and prediction. Second, many physicochemical properties, such as the charge-transfer resistance, are determined through non -unique equivalent circuits. Third, probed frequencies are usually log-spaced with a fixed number of points per decade, which is not necessarily optimal. Gaussian processes can be used to filter out noise in EIS data, determine the charge-transfer resistance as a stochastic variable, and optimize frequency placement. In this regard, a Gaussian-process-based, active-learning framework is developed to optimize EIS frequency selection for quick and accurate measurements. This work opens new avenues of research regarding the use of Gaussian processes for EIS experiment optimization.

Automated summary

Electrochemical impedance spectroscopy (EIS) is a widely used technique for studying electrochemical systems, but it has several limitations: noisy data, non-unique equivalent circuits, and suboptimal frequency placement. This study introduces a Gaussian-process-based, active-learning framework to optimize EIS frequency selection for accurate and efficient measurements. It explores the use of Gaussian processes for EIS experiment optimization.