Gaussian process regression to extract non-parametric 2-, 3- and many-body force fields.
Machine learning nonparametric force fields (MFF)
An example tutorial jupyter notebook can be found in the
Table of Contents
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 .
For an example use of the MFF package to build 3-body force fields for Ni nanoclusters, please see .
To install MFF with pip, simply run the following in a Python 3.6 or 3.7 environment:
pip install mff
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
To install from source run the following command:
python setup.py install
Or, to build in place for development, run:
python setup.py develop
Refer to the two files in the Tutorial folder for working jupyter notebooks showing most of the functionalities of this package.
- Claudio Zeni (firstname.lastname@example.org),
- Aldo Glielmo (email@example.com),
- Ádám Fekete (firstname.lastname@example.org).
 A. Glielmo, C. Zeni, A. De Vita, Efficient non-parametric n-body force fields from machine learning (https://arxiv.org/abs/1801.04823)
 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)
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