https://github.com/fernandezfran/bmxfc
:bar_chart::bulb: Datasets, pipelines and predictions of a metric for benchmarking an extreme fast-charging of Li-ion battery electrode materials
https://github.com/fernandezfran/bmxfc
benchmarking database fast-charging lithium-ion-batteries metrics pipeline prediction
Last synced: 6 months ago
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:bar_chart::bulb: Datasets, pipelines and predictions of a metric for benchmarking an extreme fast-charging of Li-ion battery electrode materials
- Host: GitHub
- URL: https://github.com/fernandezfran/bmxfc
- Owner: fernandezfran
- License: cc-by-sa-4.0
- Created: 2024-02-13T17:21:28.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-25T21:21:08.000Z (over 2 years ago)
- Last Synced: 2025-10-25T11:14:27.648Z (8 months ago)
- Topics: benchmarking, database, fast-charging, lithium-ion-batteries, metrics, pipeline, prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 687 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Datasets, pipelines and predictions of universal BMX-FC metric
[](https://www.doi.org/)
[](https://zenodo.org/doi/10.5281/zenodo.10662723)
[](https://creativecommons.org/licenses/by-sa/4.0/)
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](https://github.com/fernandezfran/bmxfc/tree/main/datasets) folder
contains the data of experimental characterizations, of the simulation of the map,
and for the validation of the model. The
[predictions](https://github.com/fernandezfran/bmxfc/tree/main/predictions) folder
contains the predictions obtained with the different
[pipelines](https://github.com/fernandezfran/bmxfc/tree/main/pipelines) that were
run in the following order:
1. [metrics.ipynb](https://github.com/fernandezfran/bmxfc/blob/main/pipelines/metrics.ipynb)
2. [predictions.ipynb](https://github.com/fernandezfran/bmxfc/blob/main/pipelines/predictions.ipynb)
3. [validations.ipynb](https://github.com/fernandezfran/bmxfc/blob/main/pipelines/validations.ipynb)
## Requirements
To run the pipelines you need [Jupyter Notebooks](https://jupyter.org/) that
require Python 3.9+ and use the
[galpynostatic](https://www.github.com/fernandezfran/galpynostatic) package, along
with other libraries from the Python data science stack such as
[matplotlib](https://matplotlib.org/), [NumPy](https://numpy.org/),
[pandas](https://pandas.pydata.org/) and [SciPy](https://scipy.org/), 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](https://github.com/fernandezfran/bmxfc/tree/main/pipelines).
## Contact
If you have any questions, you can contact me at
## Code Repository
https://www.github.com/fernandezfran/bmxfc
## License
bmxfc is licensed under the Creative Commons Attribution-ShareAlike 4.0
International License.