Datasets, pipelines and predictions of universal BMX-FC metric

Datasets, pipelines and predictions of a metric for benchmarking an extreme fast-charging of Li-ion battery electrode materials.

This repository supports the following article

F. Fernandez, E. M. Gavilán-Arriazu, D. E. Barraco, Y. Ein-Eli and E. P. M. Leiva. "A metric for benchmarking an extreme fast-charging of Li-ion battery electrode materials." Journal TODO. DOI: TODO

Content

The datasets folder contains the data of experimental characterizations, of the simulation of the map, and for the validation of the model. The predictions folder contains the predictions obtained with the different pipelines that were run in the following order:

1. metrics.ipynb
2. predictions.ipynb
3. validations.ipynb

Requirements

To run the pipelines you need Jupyter Notebooks that require Python 3.9+ and use the galpynostatic package, along with other libraries from the Python data science stack such as matplotlib, NumPy, pandas and SciPy, which can be installed as follows:

pip install -r requirements.txt

Disclaimer

This repository only have the predictions for a kinetic rate constant of 1e-7, the other values reported in the paper can be obtained by slightly modifying the pipelines.

Contact

If you have any questions, you can contact me at ffernandev@gmail.com

Code Repository

https://www.github.com/fernandezfran/bmxfc

License

bmxfc is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.