Data-driven nearest-neighbors models to predict measurement results in silicon-based lithium-ion battery anodes.
Project description
macchiato
Data-driven nearest-neighbors models to predict measurement results in silicon-based 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}
}
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