An example tutorial jupyter notebook can be found in the tutorials folder.
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].
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
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
Refer to the two files in the Tutorial folder for working jupyter notebooks showing most of the functionalities of this package.
- Claudio Zeni (claudio.zeni@kcl.ac.uk),
- Aldo Glielmo (aldo.glielmo@kcl.ac.uk),
- Ádám Fekete (adam.fekete@kcl.ac.uk).
[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)