neural force field learning toolkit
Project description
NFFLr - Neural Force Field Learning toolkit
Table of Contents
NFFLr (Introduction)
The Neural Force Field Learning library (docs) is intended to be a flexible toolkit for developing and deploying atomistic machine learning systems, with a particular focus on crystalline material property and energy models.
The initial codebase is a fork of ALIGNN, with modified configuration and modeling interfaces for performance.
Installation
Until NFFLr is registered on PyPI, it's best to install directly from github.
We recommend using a per-project pyenv-virtualenv or conda environment.
Method 1 (using setup.py):
Now, let's install the package:
git clone https://github.com/usnistgov/nfflr
cd nfflr
python -m pip install -e .
For using GPUs/CUDA, install dgl-cu101 or dgl-cu111 based on the CUDA version available on your system, e.g.
pip install dgl-cu111
Method 2 (using pypi):
Alternatively, install NFFLr directly from github using pip
:
python -m pip install https://github.com/usnistgov/nfflr
Examples
How to contribute
We gladly accept pull requests.
For detailed instructions, please see Contributing.md
Correspondence
Please report bugs as Github issues (https://github.com/usnistgov/nfflr/issues) or email to brian.decost@nist.gov.
Funding support
NIST-MGI (https://www.nist.gov/mgi).
Code of conduct
Please see Code of conduct
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.