| Name | macchiato JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | |
| Summary | Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes. |
| upload_time | 2023-12-12 02:10:08 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | |
| license | MIT License Copyright (c) 2023 Francisco Fernandez Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
data-driven-model
nearest-neighbors
clustering
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# macchiato
[](https://github.com/fernandezfran/macchiato/actions/workflows/CI.yml)
[](https://macchiato.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/macchiato/)
[](https://www.python.org/)
[](https://github.com/fernandezfran/macchiato/blob/main/LICENSE)
[](https://doi.org/10.1103/PhysRevB.108.144201)
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](https://pip.pypa.io/en/latest/)
```
python -m pip install -U pip
python -m pip install -U macchiato
```
## Usage
The Jupyter Notebook pipeline in the
[paper folder](https://github.com/fernandezfran/macchiato/tree/main/paper)
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:
```bibtex
@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 <ffernandev@gmail.com>
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