A minimal implementation of force fields
miniff: a minimal implementation of neural-network force fields
Compute energies and forces of atomic systems with neural networks.
This package implements Behler and Parrinello approach to simulating structures and dynamics of atomic systems. You may want to use it if:
- you are interested in structural properties of a system with many atoms such as a molecule or an amorphous material;
- your system is too large for electronic structure treatment (e.g. density functional theory molecular dynamics);
- no empiric potentials for your system exist ...
- ... or the existing empiric potentials are not sufficient to reach the desired tolerances in energies and forces;
- you are interested in machine learning techniques for materials science problems and are looking for a good starting point for your research.
The package is written in Python3.
- pytorch is the machine learning and autodiff driver for this package;
- cython is used for performance-critical tasks such as computing descriptors and their gradients;
- numpy is the tensor and data engine in addition to
- scipy is used for sparse data, neighbor search and optimization.
- matplotlib is used for plotting, and
- numericalunits is used for handling units.
Hosted on Read the Docs.
- C compiler: Required for compiling Cython extensions
apt-get install -y build-essential python3-dev python3-pip
dnf groupinstall "Development tools" "Development libraries"
gccand make sure it is used by default.
brew install gcc
.bash_profilepath and alias:
export CC=/usr/local/bin/gcc-10 alias gcc='gcc-10'
On Windows, the support for installing a C compiler and compiling cython extensions is cumbersome and outdated as can be inferred from the Cython Wiki. We suggest to use the Windows subsystem for Linux (WSL) in this case. More information on WSL and its installation instructions are here.
Install from git
git clone https://gitlab.kwant-project.org/qt/miniff.git cd miniff python setup.py install
Install from pip
pip install miniff
from miniff.ml_util import fit fit( prepare=dict(fn_cells="cells.json"), run=dict(n_epochs=1), )
miniff support basic command-line interface to most common workflows.
python -m miniff jobs.yml
fit: prepare: fn_cells: cells.json run: n_epochs: 1
See CONTRIBUTING.md for contirbution guidelines
See AUTHORS.MD for the list of contributors
This work is licensed under the BSD 2-Clause License
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