HotPP: High order tensor Passing Potential

Introduction

HotPP is an open-source package designed for constructing message passing network interatomic potentials. It facilitates the utilization of arbitrary order Cartesian tensors as messages while maintaining equivalence maintenance.

Current Features

• Building machine learning potentials for molecular and periodic systems;
• Learning dipole moments and polarizability tensors;
• Interface to LAMMPS and ASE;

Documentation

• An overview of code documentation and tutorials for getting started with HotPP can be found here folder.

Installation

Use pip

You can use https:

$ pip install git+https://gitlab.com/bigd4/hotpp.git

or use ssh

$ pip install git+ssh://git@gitlab.com/bigd4/hotpp.git

Your may need to add --user if you do not have the root permission. Or use --force-reinstall if you already have HotPP (add --no-dependencies if you do not want to reinstall the dependencies).

From Source

1. Use git clone to get the source code:

$ git clone https://gitlab.com/bigd4/hotpp.git

Alternatively, you can download the source code from website.

2. Go into the directory and install with pip:

$ pip install -e .

pip will read setup.py in your current directory and install. The -e option means python will directly import the module from the current path, but not copy the codes to the default lib path and import the module there, which is convenient for modifying in the future. If you do not have the need, you can remove the option.

Check

You can use

$ hotpp -v

to check if you have installed successfully

Update

If you installed by pip, use:

$ hotpp update

If you installed from source, use:

$ cd <path-to-magus-package>
$ git pull origin master

Interface

HotPP now support ASE and lammps.

ASE

LAMMPS

Contributors

HotPP is developed by Prof. Jian Sun's group at the School of Physics at Nanjing University.

The contributors are:

• Jian Sun
• Junjie Wang
• Yong Wang
• Haoting Zhang
• Ziyang Yang
• Zhixin Liang
• Jiuyang Shi

Citations

Reference	cite for what
[1]	for any work that used HotPP

Reference

[1] 1. Wang, J. et al. E(n)-Equivariant Cartesian Tensor Passing Potential. Preprint at http://arxiv.org/abs/2402.15286 (2024). (https://arxiv.org/abs/2402.15286)