macchiato 0.1.1

Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes.

macchiato

Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes.

Requirements

You need Python 3.8+ to run macchiato.

Installation

You can install the most recent stable release of macchiato with pip

python -m pip install -U pip
python -m pip install -U macchiato

Usage

The Jupyter Notebook pipeline in the paper folder is presented to reproduce the results of the published article.

Citation

Fernandez, F., Otero, M., Oviedo, M. B., Barraco, D. E., Paz, S. A., & Leiva, E. P. M. (2023). NMR, x-ray, and Mössbauer results for amorphous Li-Si alloys using density functional tight-binding method. Physical Review B, 108(14), 144201.

BibTeX entry:

@article{fernandez2023nmr,
  title={NMR, x-ray, and M{\"o}ssbauer results for amorphous Li-Si alloys using density functional tight-binding method},
  author={Fernandez, F and Otero, M and Oviedo, MB and Barraco, DE and Paz, SA and Leiva, EPM},
  journal={Physical Review B},
  volume={108},
  number={14},
  pages={144201},
  year={2023},
  publisher={APS}
}

Contact

You can contact me if you have any questions at [e-mail]