macchiato
:atom_symbol::robot: Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes
Science Score: 31.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.3%) to scientific vocabulary
Keywords
Repository
:atom_symbol::robot: Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes
Basic Info
- Host: GitHub
- Owner: fernandezfran
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://macchiato.rtfd.io/
- Size: 714 KB
Statistics
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
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:
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
Owner
- Name: Francisco Fernandez
- Login: fernandezfran
- Kind: user
- Location: Córdoba, Argentina
- Company: FAMAF, UNC
- Repositories: 3
- Profile: https://github.com/fernandezfran
Computational Physicist
Citation (CITATION.bib)
@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}
}
GitHub Events
Total
Last Year
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 0
- Total pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- fernandezfran (1)
Top Labels
Issue Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 16 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
- Total maintainers: 1
pypi.org: macchiato
Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes.
- Homepage: https://github.com/fernandezfran/macchiato
- Documentation: https://macchiato.readthedocs.io/
- 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.
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Latest release: 0.1.1
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- actions/checkout master composite
- actions/setup-python v2 composite
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- ipykernel *
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- nbsphinx *
- sphinx-rtd-theme *
- importlib_metadata *
- matplotlib *
- mdanalysis *
- numpy *
- pandas *
- pyyaml *
- scikit-learn *
- scipy *
- check-manifest * development
- coverage * development
- flake8 * development
- flake8-black * development
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