NFFLR - Neural Force Field Learning toolkit

Table of Contents

• Introduction
• Installation
• Examples
• How to contribute
• Correspondence
• Funding support

NFFLr (Introduction)

The Neural Force Field Learning library 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

Coming soon.

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 [email protected].

Funding support

NIST-MGI (https://www.nist.gov/mgi).

Code of conduct

Please see Code of conduct