How precise EIS Data can significantly Improve AI Model Accuracy

Electrochemical Impedance Spectroscopy (EIS) is a key technique for characterizing electrochemical systems. For AI-driven analysis, data quality is crucial. This white paper outlines how researchers can ensure valid EIS measurements by meeting four essential criteria: linearity, time invariance, steady-state conditions, and minimal noise. It introduces validation methods such as Lissajous plots, Kramers-Kronig tests, and model comparisons. Indicators for good quality as phase angle resolution and reproducibility are also discussed. Finally, it covers how to select appropriate equivalent circuits for meaningful interpretation. This resource is ideal for researchers aiming to generate robust datasets for AI training and electrochemical modeling.