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Yunqi Shao
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Initial public release
Coauthored with Matti Hellström, Pavlin D. Mitev, Lisanne Knijff and Chao Zhang
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‎.circleci/build_doc.sh

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#!/bin/bash
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# SHELL script for building the documentation and push to github pages
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# taken from here: https://www.alkaline-ml.com/2018-12-23-automate-gh-builds/
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set -e
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shopt -s extglob
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cd doc
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make clean html
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cd ..
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mv doc/_build/html ./
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rm -r !(".git"|"html"|".."|".")
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git checkout gh-pages
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git checkout --orphan gh-pages-tmp
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git config --global user.email "$GH_EMAIL" > /dev/null 2>&1
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git config --global user.name "$GH_NAME" > /dev/null 2>&1
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touch .nojekyll
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if [[ "$CIRCLE_BRANCH" =~ ^master$|^[0-9]+\.[0-9]+\.X$ ]]; then
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cp -r html/* ./
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else
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mkdir -p "$CIRCLE_BRANCH"
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cp -r html/* ./"$CIRCLE_BRANCH"
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fi
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rm -r html/
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git add --all
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git commit --allow-empty -m "[ci skip] Publishing updated documentation..."
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git remote rm origin
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git remote add origin https://"$GH_NAME":"$GH_TOKEN"@github.com/teoroo-cmc/PiNN.git
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git push --force origin gh-pages-tmp:gh-pages

‎.circleci/config.yml

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# Python CircleCI 2.0 configuration file
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#
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# Check https://circleci.com/docs/2.0/language-python/ for more details
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#
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version: 2
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workflows:
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version: 2
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build-and-test:
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jobs:
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- build
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- pytest:
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requires:
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- build
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- deploy-doc:
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requires:
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- build
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jobs:
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build:
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docker:
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- image: circleci/python:3.6.1
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working_directory: ~/repo
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steps:
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- checkout
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- restore_cache:
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keys:
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- venv-{{ checksum "requirements-dev.txt" }}-{{ checksum ".circleci/config.yml" }}
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- venv-
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- run:
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name: Install dependencies
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command: |
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python3 -m venv venv
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. venv/bin/activate
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pip install tensorflow==1.13.1
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pip install -r requirements-dev.txt
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- save_cache:
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paths:
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- ./venv
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key: venv-{{ checksum "requirements-dev.txt" }}-{{ checksum ".circleci/config.yml" }}
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pytest:
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docker:
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- image: circleci/python:3.6.1
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working_directory: ~/repo
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steps:
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- checkout
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- restore_cache:
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keys:
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- venv-{{ checksum "requirements-dev.txt" }}-{{ checksum ".circleci/config.yml" }}
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- run:
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name: run tests
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command: |
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. venv/bin/activate
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pip install -e .
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export CUDA_VISIBLE_DEVICES=''
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pytest tests/ --cov=pinn
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codecov --token=$CODECOV_TOKEN
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deploy-doc:
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docker:
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- image: circleci/python:3.6.1
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working_directory: ~/repo
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steps:
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- checkout
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- restore_cache:
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keys:
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- venv-{{ checksum "requirements-dev.txt" }}-{{ checksum ".circleci/config.yml" }}
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- run:
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name: Deploy doc
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command: |
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. venv/bin/activate
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sudo apt-get update
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sudo apt-get install pandoc
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pip install -e .
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./.circleci/build_doc.sh

‎.coveragerc

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‎.gitignore

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*/__pycache__
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.pytest_cache
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pinn.egg-info/
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tmp/
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*.pyc
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*~
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_build/
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.ipynb_checkpoints/
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/.coverage
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/coverage.xml

‎.readthedocs.yml

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version: 2
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# Build documentation in the docs/ directory with Sphinx
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sphinx:
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configuration: doc/conf.py
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# Optionally build your docs in additional formats such as PDF and ePub
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formats: []
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# Optionally set the version of Python and requirements required to build your docs
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python:
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version: 3.7
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install:
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- requirements: doc/requirements.txt
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- requirements: requirements-dev.txt
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- path: .

‎Dockerfile

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FROM tensorflow/tensorflow:1.13.1-py3-jupyter
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# Install PiNN
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COPY . /opt/src/pinn
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RUN pip install /opt/src/pinn && \
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pip install -r /opt/src/pinn/requirements-dev.txt && \
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pip install -r /opt/src/pinn/requirements-extra.txt && \
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jupyter nbextension enable widgetsnbextension --py --sys-prefix && \
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jupyter nbextension enable nglview --py --sys-prefix && \
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jupyter tensorboard enable --sys-prefix
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# Setup
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ENTRYPOINT ["pinn_train"]

‎Dockerfile-gpu

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FROM tensorflow/tensorflow:1.13.1-gpu-py3-jupyter
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# Install PiNN
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COPY . /opt/src/pinn
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RUN pip install /opt/src/pinn && \
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pip install -r /opt/src/pinn/requirements-dev.txt && \
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pip install -r /opt/src/pinn/requirements-extra.txt && \
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jupyter nbextension enable widgetsnbextension --py --sys-prefix && \
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jupyter nbextension enable nglview --py --sys-prefix && \
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jupyter tensorboard enable --sys-prefix
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# Setup
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ENTRYPOINT ["pinn_train"]

‎LICENSE

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BSD 3-Clause License
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Copyright (c) 2019, Teoroo-CMC
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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3. Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎README.rst

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==========================================================
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PiNN: a Python library for building atomic neural networks
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==========================================================
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.. image:: https://img.shields.io/circleci/build/github/Teoroo-CMC/PiNN/master.svg?style=flat-square&token=dd8894015481e2f87675a340fbfa712c94d69e8f
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:target: https://circleci.com/gh/Teoroo-CMC/PiNN/tree/master
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.. image:: https://img.shields.io/codecov/c/github/Teoroo-CMC/PiNN/master.svg?style=flat-square
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:target: https://codecov.io/gh/Teoroo-CMC/PiNN/branch/master
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.. image:: https://img.shields.io/docker/cloud/build/yqshao/pinn.svg?style=flat-square
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:target: https://cloud.docker.com/repository/docker/yqshao/pinn
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.. image:: https://readthedocs.org/projects/teoroo-pinn/badge/?version=latest&style=flat-square
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:target: https://teoroo-pinn.readthedocs.io/en/latest/?badge=latest
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PiNN is a Python library built on top of TensorFlow for building
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atomic neural network potentials. The PiNN library also provides
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elemental layers and abstractions to implement various atomic neural
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networks.
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The code is currenly maintained by Yunqi Shao at Uppsala Unversiy.
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Reference
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=========
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- Shao, Y.; Hellström, M.; Mitev, P. D.; Knijff, L.; Zhang, C. PiNN: A
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Python Library for Building Atomic Neural Networks of Molecules and
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Materials. arXiv:1910.03376 [cond-mat, physics:physics] 2019. `link
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<http://arxiv.org/abs/1910.03376>`_
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Requirements
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============
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- Python 3
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- TensorFlow (tested with 1.13.1)
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- ASE
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Installation
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============
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Install from source code::
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git clone https://github.com/Teoroo-CMC/PiNN.git
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cd PiNN && pip install -e .
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Or use our `docker
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image <https://cloud.docker.com/repository/docker/yqshao/pinn/tags>`_. If
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you use singularity, you can build a singularity image directly from
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the docker image::
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singularity build pinn.sif docker://yqshao/pinn:latest (or latest-gpu)
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singularity exec pinn.sif jupyter notebook # this starts a jupyter notebook server
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./pinn.sif -h # this invokes the pinn_train trainner
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Extra dependencies are in:
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- ``requirements-dev.txt``: dependency for testing and documentation building.
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- ``requirements-extra.txt``: extra libraries for various purposes, included in the docker image.
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Quick Start
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===========
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A set of tutorial notebooks can be found in the `documentation <https://teoroo-pinn.readthedocs.io/en/latest>`_.
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Models and datasets
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===================
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Dataset loaders
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---------------
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- CP2K format
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- RuNNer format
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- ANI-1 dataset
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- QM9 dataset
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Implemented Networks
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--------------------
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- PiNet
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- Behler-Parrinello Neural Network
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Implemented models
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------------------
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- Potential model
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- Dipole model
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Community
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=========
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As an open-source project, the following contributions are highly welcome:
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- Reporting bugs
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- Proposing new features
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- Discussing the current version of the code
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- Submitting fixes
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We use Github to host code, to track issues and feature requests, as well
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as to accept pull requests.
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Please follow the procedure below before you open a new issue.
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- Check for duplicate issues first.
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- If you are reporting a bug, include the system information
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(platform, Python and TensorFlow version etc.).
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If you would like to add some new features via pull request, please
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discuss with the main developer (Yunqi Shao) first to see whether it
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fits the scope and aims of this project.

‎doc/Makefile

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# Minimal makefile for Sphinx documentation
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#
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# You can set these variables from the command line.
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SPHINXOPTS =
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SPHINXBUILD = sphinx-build
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SOURCEDIR = .
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BUILDDIR = _build
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

‎doc/ase.rst

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=============
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ASE interface
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=============
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PiNN provides a ``PiNN_calc`` class to interface with ASE.
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A calculator can be created from a model as simple as:
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.. code:: python
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from pinn.models import potential_model
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from pinn.calculator import PiNN_calc
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calc = PiNN_calc(potential_modle('/path/to/model/'))
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calc.calculate(atoms)
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The implemented properties of the calculator depend on the prediciton
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returns of ``model_fn``. For example: energy, forces and stress (with
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PBC) calculations are implemented for the potential model; partial
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charge and dipole calculations are implemented for the dipole model.
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The calculator can then be used in ASE optimizers and molecular
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dynamics engines. Note that the calculator will try to use the same
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predictor (a generator given by ``estimator.predict``) whenever
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possible, so as to avoid the expensive reconstruction of the
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computation graph. However, the graph will be reconstructed if the pbc
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condition of the input ``Atoms`` is changed. Also, the predictor must
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be reset if it is interupted for some reasons.
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