https://github.com/fernandezfran/macchiato

:atom_symbol::robot: Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes
https://github.com/fernandezfran/macchiato

clustering data-driven inference model nearest-neighbors

Last synced: 15 days ago
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:atom_symbol::robot: Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes

• Host: GitHub
• URL: https://github.com/fernandezfran/macchiato
• Owner: fernandezfran
• License: mit
• Created: 2023-02-13T19:19:28.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-12-15T19:11:24.000Z (about 2 years ago)
• Last Synced: 2025-09-05T19:24:43.635Z (5 months ago)
• Topics: clustering, data-driven, inference, model, nearest-neighbors
• Language: Python
• Homepage: https://macchiato.rtfd.io/
• Size: 714 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.bib

Awesome Lists containing this project

README

          
# macchiato

[![macchiatos CI](https://github.com/fernandezfran/macchiato/actions/workflows/CI.yml/badge.svg)](https://github.com/fernandezfran/macchiato/actions/workflows/CI.yml)

[![documentation status](https://readthedocs.org/projects/macchiato/badge/?version=latest)](https://macchiato.readthedocs.io/en/latest/?badge=latest)

[![pypi version](https://img.shields.io/pypi/v/macchiato)](https://pypi.org/project/macchiato/)

[![python version](https://img.shields.io/badge/python-3.8%2B-4584b6)](https://www.python.org/)

[![mit license](https://img.shields.io/badge/License-MIT-ffde57)](https://github.com/fernandezfran/macchiato/blob/main/LICENSE)

[![PRB](https://img.shields.io/badge/PhysRevB-108.144201-b31033)](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