Machine learning assisted analysis of equivalent circuit usage in electrochemical impedance spectroscopy applications

Abstract

AbstractElectrical equivalent circuits are a widely applied tool with which electrical processes can be rationalized. There is a wide‐ranging selection of fields from bioelectrochemistry to batteries to fuel cells making use of this tool. Enabling meta‐analysis on the similarities and differences in the used circuits will help to identify commonly used circuits and aid in evaluating the underlying physics. We present a method and an implementation that enables the conversion of circuits included in scientific publications into a machine‐readable form for generating machine learning datasets or circuit simulations.