Metadata-Version: 2.1
Name: mir-flare
Version: 0.1.3
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/) [![activity](https://img.shields.io/github/commit-activity/m/mir-group/flare)](https://github.com/mir-group/flare/commits/master) [![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
        
        <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>
        
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
