{
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        "author": "Materials Intelligence Research",
        "author_email": "mir@g.harvard.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": "[![Build Status](https://travis-ci.org/mir-group/flare.svg?branch=master)](https://travis-ci.org/mir-group/flare) [![documentation](https://readthedocs.org/projects/flare/badge/?version=latest)](https://readthedocs.org/projects/flare) [![pypi](https://img.shields.io/pypi/v/mir-flare)](https://pypi.org/project/mir-flare/) [![activity](https://img.shields.io/github/commit-activity/m/mir-group/flare)](https://github.com/mir-group/flare/commits/master) [![codecov](https://codecov.io/gh/mir-group/flare/branch/master/graph/badge.svg)](https://codecov.io/gh/mir-group/flare)\n\n# FLARE: Fast Learning of Atomistic Rare Events\n\nFLARE is an open-source Python package for creating fast and accurate atomistic potentials. Documentation of the code is in progress, and can be accessed here: https://flare.readthedocs.io/\n\n\n## Prerequisites\n1. To train a potential on the fly, you need a working installation of [Quantum ESPRESSO](https://www.quantum-espresso.org) or [CP2K](https://www.cp2k.org).\n2. FLARE requires Python 3 with the packages specified in `requirements.txt`. This is taken care of by `pip`.\n\n## Installation\nFLARE can be installed in two different ways.\n1. Download and install automatically:\n    ```\n    pip install mir-flare\n    ```\n2. Download this repository and install (required for unit tests):\n    ```\n    git clone https://github.com/mir-group/flare\n    cd flare\n    pip install .\n    ```\n\n\n## Tests\nWe recommend running unit tests to confirm that FLARE is running properly on your machine. We have implemented our tests using the pytest suite. You can call `pytest` from the command line in the tests directory to validate that Quantum ESPRESSO or CP2K are working correctly with FLARE.\n\nInstructions (either DFT package will suffice):\n```\npip install pytest\ncd tests\nPWSCF_COMMAND=/path/to/pw.x CP2K_COMMAND=/path/to/cp2k pytest\n```\n\n## References\n[1] Jonathan Vandermause, Steven B. Torrisi, Simon Batzner, Alexie M. Kolpak, and Boris Kozinsky. *On-the-fly Bayesian active learning of interpretable force fields for atomistic rare events.* https://arxiv.org/abs/1904.02042\n\n\n",
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