yqshao/pinn repository overview

⁠========================================================== PiNN: a Python library for building atomic neural networks

.. image:: https://img.shields.io/circleci/build/github/Teoroo-CMC/PiNN/master.svg?style=flat-square&token=dd8894015481e2f87675a340fbfa712c94d69e8f⁠ :target: https://circleci.com/gh/Teoroo-CMC/PiNN/tree/master⁠

.. image:: https://img.shields.io/codecov/c/github/Teoroo-CMC/PiNN/master.svg?style=flat-square⁠ :target: https://codecov.io/gh/Teoroo-CMC/PiNN/branch/master⁠

.. image:: https://img.shields.io/docker/cloud/build/yqshao/pinn.svg?style=flat-square⁠ :target: https://cloud.docker.com/repository/docker/yqshao/pinn⁠

.. image:: https://readthedocs.org/projects/teoroo-pinn/badge/?version=latest&style=flat-square⁠ :target: https://teoroo-pinn.readthedocs.io/en/latest/?badge=latest⁠

PiNN is a Python library built on top of TensorFlow for building atomic neural network potentials. The PiNN library also provides elemental layers and abstractions to implement various atomic neural networks.

The code is currenly maintained by Yunqi Shao at Uppsala Unversiy.

⁠Reference

• Shao, Y.; Hellström, M.; Mitev, P. D.; Knijff, L.; Zhang, C. PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials. arXiv:1910.03376 [cond-mat, physics:physics] 2019. link <http://arxiv.org/abs/1910.03376>_

⁠Requirements

• Python 3
• ASE, Numpy, Pyyaml
• TensorFlow >= 1.15.2 [#tf_version]_

⁠Installation

Install from source code::

git clone https://github.com/Teoroo-CMC/PiNN.git⁠ cd PiNN && pip install -e .

Or use our docker image <https://cloud.docker.com/repository/docker/yqshao/pinn/tags>_. If you use singularity, you can build a singularity image directly from the docker image::

singularity build pinn.sif docker://yqshao/pinn:latest (or latest-gpu) singularity exec pinn.sif jupyter notebook # this starts a jupyter notebook server ./pinn.sif -h # this invokes the pinn_train trainner

Extra dependencies are in:

• requirements-dev.txt: dependency for testing and documentation building.
• requirements-extra.txt: extra libraries for various purposes, included in the docker image.

⁠Quick Start

A set of tutorial notebooks can be found in the documentation <https://teoroo-pinn.readthedocs.io/en/latest>_.

⁠Models and datasets

⁠Dataset loaders

• CP2K format
• RuNNer format
• ANI-1 dataset
• QM9 dataset

⁠Implemented Networks

• PiNet
• Behler-Parrinello Neural Network

⁠Implemented models

• Potential model
• Dipole model

⁠Community

As an open-source project, the following contributions are highly welcome:

• Reporting bugs
• Proposing new features
• Discussing the current version of the code
• Submitting fixes

We use Github to host code, to track issues and feature requests, as well as to accept pull requests.

Please follow the procedure below before you open a new issue.

• Check for duplicate issues first.
• If you are reporting a bug, include the system information (platform, Python and TensorFlow version etc.).

If you would like to add some new features via pull request, please discuss with the main developer (Yunqi Shao) first to see whether it fits the scope and aims of this project.

⁠Notes

.. [#tf_version] TensorFlow is not installed automatically by default. This dependency can be included by appending the [gpu] option when installing PiNN with pip. Otherwise, you can install PiNN with CPU-only tensorflow using the [cpu] option.