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<p>In this work, we present a user-friendly tool for simulating electrochemical impedance spectroscopy (EIS) data. The framework generates a distribution function of relaxation times (DRT, also known as DFRT) based on user-defined parameters and converts it into the corresponding impedance spectrum. The generated datasets can be saved as csv files. This tool can be used to evaluate the performance of DRT-based EIS data analysis algorithms and help validate the results. Additionally, our framework can generate large synthetic EIS datasets needed for training machine learning models. We demonstrate the tool’s capabilities and potential applications by simulating EIS data and analyzing it with the impedance spectroscopy genetic programming (ISGP) algorithm to evaluate its performance. This development paves the way for extending the modeling to other electrochemical phenomena in the time domain and identifying their corresponding DRT representations.</p>

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A flexible tool for generating synthetic EIS data using the distribution of relaxation times approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月5日

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  • Alaa Egbaria,
  • Shemaya Zabari,
  • Yoed Tsur

摘要

In this work, we present a user-friendly tool for simulating electrochemical impedance spectroscopy (EIS) data. The framework generates a distribution function of relaxation times (DRT, also known as DFRT) based on user-defined parameters and converts it into the corresponding impedance spectrum. The generated datasets can be saved as csv files. This tool can be used to evaluate the performance of DRT-based EIS data analysis algorithms and help validate the results. Additionally, our framework can generate large synthetic EIS datasets needed for training machine learning models. We demonstrate the tool’s capabilities and potential applications by simulating EIS data and analyzing it with the impedance spectroscopy genetic programming (ISGP) algorithm to evaluate its performance. This development paves the way for extending the modeling to other electrochemical phenomena in the time domain and identifying their corresponding DRT representations.