AI-Guided Optimization of EIS Measurements: Minimizing Low-Frequency Sampling for Data-Efficient Electrochemical Characterization

Qiuyu Shi, Naohiro Fujinuma, Yonatan Kurniawan, RUNZE ZHANG, Ali Jaberi, Ashley S. Dale, Jason Hattrick-Simpers

Submission Type: I want my submission to be considered for both oral and poster presentation.

Keywords: Physics-Informed Machine Learning, Bayesian Inference, Electrochemical Impedance Spectroscopy, Data Efficiency, Uncertainty Quantification

TL;DR: We present a physics-informed Bayesian framework that reconstructs full EIS spectra by augmenting high-frequency data, effectively reducing the need for noisy low-frequency measurements while maintaining model fidelity

Confirmation Of Submission Requirements: I submit an abstract. It uses the template provided on the submission page and is no longer than 2 pages.

Submission Number: 411