Fast Learning of Atomistic Rare Events
FLARE: Fast Learning of Atomistic Rare Events
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
- Coupling to Quantum Espresso, CP2K, and VASP DFT engines
Mapped Gaussian Processes
- Mapping to efficient cubic spline models
- ASE calculator for GP models
- On-the-fly training with ASE MD engines
Module for training GPs from AIMD trajectories
- 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).
- FLARE requires Python 3 with the packages specified in
requirements.txt. This is taken care of by
FLARE can be installed in two different ways.
- Download and install automatically:
pip install mir-flare
- 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:
 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:
 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
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