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

Electrical 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.