Metadata-Version: 2.1
Name: mir-flare
Version: 0.0.2
Summary: Fast Learning of Atomistic Rare Events
Home-page: https://github.com/mir-group/flare
Author: Materials Intelligence Research
Author-email: mir@g.harvard.edu
License: MIT
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/) [![codecov](https://codecov.io/gh/mir-group/flare/branch/master/graph/badge.svg)](https://codecov.io/gh/mir-group/flare)
        
        # FLARE: Fast Learning of Atomistic Rare Events
        
        FLARE 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/
        
        
        ## Prerequisites
        1. 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).
        2. FLARE requires Python 3 with the packages specified in `requirements.txt`. This is taken care of by `pip`.
        
        ## Installation
        FLARE can be installed in two different ways.
        1. Download and install automatically:
            ```
            pip install mir-flare
            ```
        2. Download this repository and install (required for unit tests):
            ```
            git clone https://github.com/mir-group/flare
            cd flare
            pip install .
            ```
        
        
        ## Tests
        We 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.
        
        Instructions (either DFT package will suffice):
        ```
        pip install pytest
        cd tests
        PWSCF_COMMAND=/path/to/pw.x CP2K_COMMAND=/path/to/cp2k pytest
        ```
        
        ## References
        [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
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.6
Description-Content-Type: text/markdown
