AutoEIS

What is AutoEIS?

AutoEIS is a Python package that automatically proposes statistically plausible equivalent circuit models (ECMs) for electrochemical impedance spectroscopy (EIS) analysis. The package is designed for researchers and practitioners in the fields of electrochemical analysis, including but not limited to explorations of electrocatalysis, battery design, and investigations of material degradation.

Please be aware that the current version is still under development and has not been formally released. If you find any bugs or have any suggestions, please file an issue or directly submit a pull request. We would greatly appreciate any contributions from the community.

Installation

Follow the instructions below to install AutoEIS and its dependencies:

Install Julia

The official way to install Julia is via juliaup. Juliaup provides a command line interface to automatically install Julia (optionally multiple versions side by side). Working with juliaup is straightforward; Please follow the instructions on its GitHub page.

Install AutoEIS using pip

Open a terminal (or command prompt on Windows) and run the following command:

pip install -U autoeis

pip install "jax[cpu]===0.4.11" -f https://whls.blob.core.windows.net/unstable/index.html --use-deprecated legacy-resolver

pip install jax[cuda111] -f https://whls.blob.core.windows.net/unstable/index.html --use-deprecated legacy-resolver

Install Julia dependencies

The circuit generation is done using the Julia package EquivalentCircuits.jl. AutoEIS provides a helper function to automatically install the required Julia dependencies. Open a terminal (or command prompt on Windows) and run the following command:

python -m autoeis install

Note: AutoEIS doesn't pollute your global Julia environment. Instead, it creates a new shared environment called autoeis-VERSION_NUMBER (VERSION_NUMBER is the AutoEIS version) and installs the required packages there. This way, you can safely use AutoEIS without worrying about breaking your existing Julia environment. Shared environments are stored in the ~/.julia/environments directory on Unix-based systems and %USERPROFILE%\.julia\environments on Windows.

Verify the installation

If all steps were completed successfully, you should now be able to use AutoEIS. To confirm that AutoEIS is installed correctly, running the following command in a terminal (or command prompt on Windows) should print the version number:

python -c "import autoeis; print(autoeis.__version__)"

Workflow

The schematic workflow of AutoEIS is shown below:

It includes: data pre-processing, ECM generation, circuit post-filtering, Bayesian inference, and the model evaluation process. Through this workflow, AutoEis can prioritize the statistically optimal ECM and also retain suboptimal models with lower priority for subsequent expert inspection. A detailed workflow can be found in the paper.

Usage

To use AutoEIS, you can either perform the circuit generation and Bayesian inference step by step or use the perform_full_analysis function to perform the whole process automatically. The following is an example of how to use the perform_full_analysis function:

import numpy as np
import autoeis as ae

# Load test dataset shipped with AutoEIS
Z, freq = ae.io.load_test_dataset()

# Perform automated EIS analysis
circuits = ae.perform_full_analysis(Z, freq, iters=100, parallel=True)
print(circuits)

• Z: Electrochemical impedance measurements (complex array)
• freq: Frequencies corresponding to the impedance measurements
• iters: Numbers of equivalent circuit generation to be performed
• parallel: Whether to use parallel processing to speed up the analysis

An example notebook that demonstrates how to use AutoEIS can be found here.

Work in progress/known issues

• Refactor the code as it is still in the developmental stage and not yet production-ready.
• Speed up the code; Currently, it takes ~ 4 hours to generate 100 equivalent circuits (your mileage may vary).
• Add proper unit/integration tests.
• Add proper documentation (API, more examples, etc.).
• Add a graphical user interface for a more user-friendly interaction.

Acknowledgement

The authors extend their heartfelt gratitude to the following individuals for their invaluable guidance and support throughout the development of this work: Prof. Jason Hattrick-Simpers, Dr. Robert Black, Dr. Debashish Sur, Dr. Parisa Karimi, Dr. Brian DeCost, Dr. Kangming Li, and Prof. John R. Scully.

The authors also wish to express their sincere appreciation to the following experts for engaging in technical discussions and providing valuable feedback: Dr. Shijing Sun, Prof. Keryn Lian, Dr. Alvin Virya, Dr. Austin McDannald, Dr. Fuzhan Rahmanian, and Prof. Helge Stein.

Special thanks go to Prof. John R. Scully and Dr. Debashish Sur for graciously allowing us to utilize their corrosion data as an illustrative example to showcase the functionality of AutoEIS. Their contributions have been immensely helpful in shaping this research, and their unwavering support is deeply appreciated.