Machine Learning-based Classification of Electrochemical Impedance Spectra

This repository contains the code to the corresponding publication: "Machine learning benchmarks for the classification of equivalent circuit models from solid-state electrochemical impedance spectra" http://arxiv.org/abs/2302.03362

Setup

A requirements.txt file and, alternatively, a file environment.yml is provided to create the python environment needed to run this code.

Firstly to set up the environment, please run the following:

conda create -n "eis-ml" python=3.9.15 ipython
conda activate eis-ml
pip install -r requirements.txt

If you are on MacOS you might have to install tensorflw and tsfresh separately/manually fix the installation of subdependdecies etc.

Workflow

Run the preprocess.py file to calculate all required data, features, and images. (The GitHub repository comes with the data for the RF and CNN models. The tsfresh feature files are too large; therefore, this step is required if you want to run the xgb model.)
Run the model of your choice. The results are automatically saved in the respective results folder. The name of the folder is based on the timestamp.

Notebooks: data_vis_and_exploration.ipynb: Exploring the data and making plots.

.py files: utils.py Code to make plots utils_preprocessing.py Code to preprocess the .csv files with the spectra

preprocess.py Preprocessing of data

clf_rf.py Random forest model python script (train, test, save results) clf_xgb.py XGB model python script (train, test, save results) clf_cnn.py CNN model python script (train, test, save results)

eis folder: EIS toolkit that Raymond Gasper wrote for the BatteryDEV competition. Includes advanced options to simulate EIS data, visualize EIS data, and optimize equivalent circuit parameters based on initial guesses.

miscellaneous folder: Contains code to reproduce results shown in the supplementary information

Contribute

We welcome any further contributions to this repository. If you find bucks or are interested in new features and capabilities, please raise an issue or propose a pull request.

Data

This repository contains EIS spectra provided by QuantumScape, capturing about ten years of R&D. The dataset consists of labeled data (~10k spectra). This means spectra with an associated Equivalent Circuit Model (ECM) with parameters estimated by Quantum Scape engineers. The labeled dataset is split into training and test dataset: data/train_data.csv and data/test_data.csv Furthermore, the repository contains unlabeled spectra (~19k spectra): data/unlabeled_data.csv. The data in this article is shared under the terms of the CC-BY 4.0 license according to the file data\LICENSE. We thank Tim Holme from Quantum Scape for providing this dataset.

License

The codecode in this repository is made publicly available under the terms of the MIT license as denoted in the LICENSE file. The data in this article is shared under the terms of the CC-BY 4.0 license according to the file data\LICENSE.

Acknowledment/Citation

If you use code from this repositroy for your work, please cite:

@misc{ECM_classficiation_EIS_23,
author = {Schaeffer, Joachim and Gasper, Paul and Garcia-Tamayo, Esteban and Gasper, Raymond and Adachi, Masaki and Gaviria-Cardona, Juan Pablo and Montoya-Bedoya, Simon and Bhutani, Anoushka and Schiek, Andrew and Goodall, Rhys and Findeisen, Rolf and Braatz, Richard D. and Engelke, Simon},
keywords = {Machine Learning (cs.LG), Materials Science (cond-mat.mtrl-sci), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences, 68T10},
title = {Machine learning benchmarks for the classification of equivalent circuit models from solid-state electrochemical impedance spectra},
note = {arXiv preprint, https://arxiv.org/abs/2302.03362},
eprint = {2302.03362},
archiveprefix = {arXiv},
doi = {10.48550/ARXIV.2302.03362},
year = {2023}
}

jherfson / AutoECM