Metadata-Version: 2.1
Name: mff
Version: 1.1.2
Summary: Gaussian process regression to extract non-parametric 2-, 3- and many-body force fields.
Home-page: https://github.com/kcl-tscm/mff
Author: Claudio Zeni, Adam Fekete, Aldo Glielmo
Author-email: 
License: UNKNOWN
Description: # Machine learning nonparametric force fields (MFF)
        [![Build Status](https://travis-ci.com/kcl-tscm/mff.svg?branch=master)](https://travis-ci.com/kcl-tscm/mff)
        [![Doc](https://img.shields.io/badge/docs-master-blue.svg)](https://kcl-tscm.github.io/mff/)
        [![DOI](https://zenodo.org/badge/123019663.svg)](https://zenodo.org/badge/latestdoi/123019663)
        
        An example tutorial jupyter notebook can be found in the `tutorials` folder.
        
        ![alt text](https://kcl-tscm.github.io/mff/_static/mff_logo_2.svg)
        ## Table of Contents
        
        - [Background on MFFs](#background)
        - [Install](#install)
        - [Examples](#examples)
        - [Maintainers](#maintainers)
        - [References](#references)
        
        ## Background on MFF
        
        The MFF package uses Gaussian process regression to extract non-parametric 2- and 3- body force fields from ab-initio calculations.
        For a detailed description of the theory behind Gaussian process regression to predict forces and/or energies, and an explanation of the mapping technique used, please refer to [1].
        
        For an example use of the MFF package to build 3-body force fields for Ni nanoclusters, please see [2].
        
        ## Pip Installation
        
        To install MFF with pip, simply run the following in a Python 3.6 or 3.7 environment:
        
            pip install mff
        
        
        ## Source Installation
        
        If the pip installation fails, try the following:
        Clone from source and enter the folder:
        
            git clone https://github.com/kcl-tscm/mff.git
            cd mff
        
        
        If you don't have it, install virtualenv:
        
            pip install virtualenv	   
        
        
        Create a virtual environment using a python 3.6 installation	
        
        	virtualenv --python=/usr/bin/python3.6 <path/to/new/virtualenv/>	
        
        
        Activate the new virtual environment 	
        
        	source <path/to/new/virtualenv/bin/activate>	
        
        
        To install from source run the following command:	
        
            python setup.py install	
        
        
        Or, to build in place for development, run:	
        
            python setup.py develop
        
        
        
        ## Examples
        Refer to the two files in the Tutorial folder for working jupyter notebooks showing most of the functionalities of this package.
        
        
        ## Maintainers
        
        * Claudio Zeni (claudio.zeni@kcl.ac.uk),
        * Aldo Glielmo (aldo.glielmo@kcl.ac.uk),
        * Ádám Fekete (adam.fekete@kcl.ac.uk).
        
        ## References
        
        [1] A. Glielmo, C. Zeni, A. De Vita, *Efficient non-parametric n-body force fields from machine learning* (https://arxiv.org/abs/1801.04823)
        
        [2] C .Zeni, K. Rossi, A. Glielmo, A. Fekete, N. Gaston, F. Baletto, A. De Vita *Building machine learning force fields for nanoclusters* (https://arxiv.org/abs/1802.01417)
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
