Skip to content
forked from mir-group/flare

An open-source Python package for creating fast and accurate atomistic potentials.

License

Notifications You must be signed in to change notification settings

mayankaditya/flare

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Build Status documentation pypi activity codecov

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 or CP2K.
  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

About

An open-source Python package for creating fast and accurate atomistic potentials.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 96.8%
  • Jupyter Notebook 3.1%
  • Shell 0.1%