Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes.
You need Python 3.8+ to run macchiato.
You can install the most recent stable release of macchiato with pip
python -m pip install -U pip
python -m pip install -U macchiato
The Jupyter Notebook pipeline in the paper folder is presented to reproduce the results of the published article.
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}
}You can contact me if you have any questions at ffernandev@gmail.com