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journal article·2020-04-01Open access

Bayesian statistics-based analysis of AC impedance spectra

Yu Miyazaki, Ryo Nakayama, Nobuaki Yasuo, Yuki Watanabe, Ryota Shimizu, Daniel M. Packwood, Kazunori Nishio, Yasunobu Ando, Masakazu Sekijima, Taro Hitosugi

AIP Advances· Volume 10· Issue 4
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Source abstract

Abstract

AC impedance spectroscopy is an important method for evaluating ionic, electronic, and dielectric properties of materials. In conventional analysis of AC impedance spectra, the selection of an equivalent circuit model and its initial parameters are visually determined from a Nyquist plot; this visual determination can be both inefficient and inaccurate. Thus, analysis based on a rigorous mathematical method is highly desirable. Here, we demonstrate the analysis of AC impedance spectra using Bayesian statistics. We apply the method to artificial AC impedance spectra generated from resistance (R) and capacitance (C) circuits, obtaining a high accuracy ratio (>90%) in model selection when the ratio of the time constants of two RC parallel circuits exceeds 3. Furthermore, this method is applied to an actual electrical circuit comprising a resistance and two RC parallel circuits, yielding highly accurate model selection and parameter estimation. The results demonstrate the effectiveness of the proposed method for AC impedance spectra.
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Research context

OpenAlex topics: Fault Detection and Control Systems · Electrochemical Analysis and Applications · Machine Learning in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan

Department of Computer Science, Tokyo Institute of Technology 2 , Nagatsuta, Yokohama 4259-J3-23, Japan

PRESTO, Japan Science and Technology Agency 3 , Kawaguchi, Saitama 332-0012, Japan

Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University 4 , Kyoto 606-8501, Japan

Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology 5 , Tsukuba, Ibaraki 305-8568, Japan

Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology 6 , Nagatsuta, Yokohama 4259-J3-23, Japan

Funding

JKA · Japan Society for the Promotion of Science · Core Research for Evolutional Science and Technology · Toyota Motor Corporation

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