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        "author": "Olivier Malenfant-Thuot",
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        "description": "# ML\\_Calc\\_Driver\n\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)\n[![Build Status](https://travis-ci.org/OMalenfantThuot/ML_Calc_Driver.svg?branch=master&kill_cache=1)](https://travis-ci.org/OMalenfantThuot/ML_Calc_Driver)\n[![Coverage Status](https://coveralls.io/repos/github/OMalenfantThuot/ML_Calc_Driver/badge.svg?branch=master&kill_cache=1)](https://coveralls.io/github/OMalenfantThuot/ML_Calc_Driver)\n[![PyPi](https://img.shields.io/pypi/v/mlcalcdriver.svg?kill_cache=1)](https://pypi.org/project/mlcalcdriver/)\n[![python](https://img.shields.io/pypi/pyversions/mlcalcdriver.svg)](https://www.python.org/)\n[![PyUp](https://pyup.io/repos/github/OMalenfantThuot/ML_Calc_Driver/shield.svg?kill_cache=1)](https://pyup.io/repos/github/OMalenfantThuot/ML_Calc_Driver)\n\nML\\_Calc\\_Driver is a driver to emulate DFT calculations using machine learned predictive models.\nTo use this package, one needs an already trained model, that can predict energy or forces from an input atomic geometry.\nSupported systems depends on the model, this package should not be a limitation.\n\nThis package has been tested with python 3.7, but should work with older python 3 versions.\n\nCredit to [mmoriniere](https://gitlab.com/mmoriniere) for the [MyBigDFT package](https://gitlab.com/mmoriniere/MyBigDFT)\nwhich served as a foundation for this work. Some classes have been directly adapted from MyBigDFT.\n\n## Documentation\n\nNo documentation for the moment.\n\n## Installation\n\n### From PyPi\n\nTo install the latest version available on PyPi:\n\n`pip install mlcalcdriver`\n\nTo upgrade to last version:\n\n`pip install -U mlcalcdriver`\n\n### From sources\n\n```\ngit clone git@github.com:OMalenfantThuot/ML_Calc_Driver.git\ncd ML_Calc_Driver\npip install .\n```\n\nTo modify the sources, instead of `pip install .`, use\n\n```\npip install -r requirements_dev.txt\npip install -e .\n```\n\n### To use with [SchetPack](https://github.com/atomistic-machine-learning/schnetpack) trained models (only supported models at the time)\n\n`pip install schnetpack`\n\n\n",
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