Metadata-Version: 2.1
Name: mir-flare
Version: 0.1.6
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
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
Requires-Dist: numpy (>=1.20.1)
Requires-Dist: scipy
Requires-Dist: memory-profiler
Requires-Dist: numba
Requires-Dist: ase
Requires-Dist: pymatgen
Requires-Dist: nptyping
Requires-Dist: nbsphinx
Requires-Dist: IPython
Requires-Dist: pytest (>=4.6)

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# FLARE: Fast Learning of Atomistic Rare Events

<p align="center">
  <img width="659" height="303" src="https://github.com/mir-group/flare/blob/master/docs/images/Flare_logo.png?raw=true">
</p>

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

We have an introductory tutorial in Google Colab available [here](https://colab.research.google.com/drive/1Q2NCCQWYQdTW9-e35v1W-mBlWTiQ4zfT).

## Major Features

* 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


## 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
- 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., Cepellotti, A. & Kozinsky, B. *Fast bayesian force fields from active learning: study of inter-dimensional transformation of stanene.* arXiv:2008.11796 [cond-mat, physics:physics] (2020). at <http://arxiv.org/abs/2008.11796>


