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
We recommend using a per-project pyenv-virtualenv or conda environment.
To ensure proper CUDA support, make sure to install the GPU versions of PyTorch and DGL. For example, to set up a conda environment on linux with with python 3.10 and CUDA 12.1:
conda create --name myproject python=3.10
conda activate myproject
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
conda install -c dglteam/label/cu121 dgl
python -m pip install 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
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