High Dimensional Neural Network Potential(HDNNP)

This is a implementation of High Dimensional Neural Network Potential(HDNNP) designed to reproduce Density Function Theory(DFT) calculation effectively with high flexibility, reactivity.

The more detailed docs https://hdnnp.readthedocs.io/en/latest/index.html

There is equivalent doc in Japanese README.ja.md.

Install

Install this project by git.

$ git clone https://github.com/ogura-edu/HDNNP.git

# or if using ssh

$ git clone git@github.com:ogura-edu/HDNNP.git

This project uses Pipenv for development workflow. If you don't have it, run this command to install.

macOS

$ brew install pipenv

other

# please run after installing python 
$ pip install pipenv

Setup

By Pipenv(Prefered)

Same as by anaconda, but you need to install python rather than installing anaconda.

This bug will be fixed in near future release(ref: pythonfinder + pyenv + anaconda issue).

Set environmental variable PIPENV_VENV_IN_PROJECT to 1 to create your VM into this project dir(/path/to/HDNNP/.venv).

export PIPENV_VENV_IN_PROJECT = 1

For macOS users, you need to install mpich before installing dependencies.

# Only for macOS users. 
#
# NOTE: Installing both mpich and openmpi will conflict
#
$ brew install mpich

# or

$ brew install openmpi

Setup your enviroments.

# Install dependencies
$ pipenv install --dev

# activate your VM
$ pipenv shell

# For example...
(HDNNP) $ hdnnpy training

# deactivate
(HDNNP) $ exit

By Anaconda

Using anaconda is prefered because it is basically faster than Pipenv.

Install anaconda and activate your VM.

$ ANACONDA_VERSION = [YOUR_ANACODA_VERSION]
$ pyenv install $ANACONDA_VERSION
$ pyenv local $ANACONDA_VERSION
$ conda env create -n HDNNP --file condaenv.yaml
$ echo ". ${HOME}/.pyenv/versions/<anacondaVERSION>/etc/profile.d/conda.sh" > ~/.bashrc

# activate
$ conda activate HDNNP

# install this program using pip
(HDNNP) $ pip install --editable .

# For example...
(HDNNP) $ hdnnpy training

# deactivate
(HDNNP) $ conda deactivate

NOTE

There is no

ChainerMN
Chainer v5

on the Anaconda Cloud, so you still have to install these packages by pip.

And these is a bug that if you install anaconda by pyenv, pipenv will fail to start(ref: pythonfinder + pyenv + anaconda issue).

Reference

Jörg Behler. First Principle Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed System, 2007

eminamitani / hdnnpy-update