The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Quattrocchi, Emanuele; Wan, Ting Hei; Belotti, Alessio; Kim, Dohyung; Pepe, Simona; Kalinin, Sergei V.; Ahmadi, Mahshid; Ciucci, Francesco

doi:10.1016/j.electacta.2021.139010

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Journal Article · Sat Jul 31 00:00:00 EDT 2021 · Electrochimica Acta

DOI:https://doi.org/10.1016/j.electacta.2021.139010· OSTI ID:1831633

Quattrocchi, Emanuele ^[1]; ^[1]; ^[1]; ^[2]; Pepe, Simona ^[1]; ^[3]; ^[2]; Ciucci, Francesco ^[1]

Hong Kong Univ. of Science and Technology (Hong Kong)
Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Electrochemical impedance spectroscopy (EIS) is an experimental technique ubiquitously used to study electrochemical systems. However, conventional EIS data interpretation through physical and equivalent circuit models is challenging because physical models are problem-specific, and equivalent circuits are often just lumped-element analogs lacking physical meaning. The distribution of relaxation times (DRT) has emerged as a complementary approach to resolve these issues. One drawback of conventional DRT deconvolution is that the EIS data is understood to be (only) a function of frequency (i.e. 1D data) and deconvolved accordingly. This work proposes a novel deconvolution method based on deep neural networks (DNNs), allowing the analysis of multidimensional EIS spectra to bridge data dependency on both frequencies and experimental conditions. Two particularly appealing traits of the deep-DRT method developed in this article are that neither regularization nor specific spacing on the state variables defining the experiment are required. Finally, leveraging DNN to examine complex EIS spectra and their dependence on experimental conditions, this work opens a new research direction in the area of EIS analysis and DRT deconvolution.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1831633

Journal Information:: Electrochimica Acta, Journal Name: Electrochimica Acta Journal Issue: 1 Vol. 392; ISSN 0013-4686

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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