FLARE is an open-source Python package for creating fast and accurate interatomic potentials. Documentation of the code can be accessed here: https://flare.readthedocs.io/

We have an introductory tutorial in Google Colab available here.

• Gaussian Process Force Fields

  • 2- and 3-body multi-element kernels
  • Maximum likelihood hyperparameter optimization

• On-the-Fly Training

  • Coupling to Quantum Espresso, CP2K, and VASP DFT engines

• Mapped Gaussian Processes

  • Mapping to efficient cubic spline models

• ASE Interface

  • ASE calculator for GP models
  • On-the-fly training with ASE MD engines

• Module for training GPs from AIMD trajectories

If you want to train a more accurate many-body potential on the fly, check out our many-body extension flare++, with an introductory tutorial available here.

1. To train a potential on the fly, you need a working installation of a DFT code compatible with ASE (e.g. Quantum ESPRESSO, CP2K, or VASP).
2. FLARE requires Python 3 with the packages specified in requirements.txt. This is taken care of by pip.

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 .
   

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

• If you use FLARE in your research, or any part of this repo (such as the GP implementation), please cite the following paper:

  [1] Vandermause, J., Torrisi, S. B., Batzner, S., Xie, Y., Sun, L., Kolpak, A. M. & Kozinsky, B. On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events. npj Comput Mater 6, 20 (2020). https://doi.org/10.1038/s41524-020-0283-z

• If you use MGP or LAMMPS pair style, please cite the following paper:

  [2] Xie, Y., Vandermause, J., Sun, L. et al. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene. npj Comput Mater 7, 40 (2021). https://doi.org/10.1038/s41524-021-00510-y