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preferred-citation:
type: article
authors:
- family-names: "Wang"
given-names: "Han"
- family-names: "Zhang"
given-names: "Linfeng"
- family-names: "Han"
given-names: "Jiequn"
- family-names: "E"
given-names: "Weinan"
doi: "10.1016/j.cpc.2018.03.016"
journal: "Computer Physics Communications"
month: 7
start: 178 # First page number
end: 184 # Last page number
title: "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics"
volume: 228
year: 2018
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# DeePMD-kit Contributing Guide
Welcome to [DeePMD-kit](https://github.com/deepmodeling/deepmd-kit)!
## What you can contribute
You can either make a code contribution, help improve our document or offer help to other users. Your help is always appreciated. Come and have fun!
### Code contribution
You can start from any one of the following items to help improve deepmd-kit
- Smash a bug
- Implement a feature or add a patch, whatever you think deepmd-kit is missing
- Browse [issues](https://github.com/deepmodeling/deepmd-kit/issues), find an issue labeled enhancement or bug, and help to solve it.
See [here](#before-you-contribute) for some before-hand heads-up.
See [here](#how-to-contribute) to learn how to contribute.
### Document improvement
You can start from any one of the following items to help improve [DeePMD-kit Docs](https://deepmd.readthedocs.io/en/latest/?badge=latest):
- Fix typos or format (punctuation, space, indentation, code block, etc.)
- Fix or update inappropriate or outdated descriptions
- Add missing content (sentence, paragraph, or a new document)
- Translate docs changes from English to Chinese
### Offer help
You can help other users of deepmd-kit in the following way
- Submit, reply to, and resolve [issues](https://github.com/deepmodeling/deepmd-kit/issues)
- (Advanced) Review Pull Requests created by others
## Before you contribute
### Overview of DeePMD-kit
Currently, we maintain two main branch:
- master: stable branch with version tag
- devel : branch for developers
### Developer guide
See [here](doc/development/index.md) for coding conventions, API and other needs-to-know of the code.
## How to contribute
Please perform the following steps to create your Pull Request to this repository. If don't like to use commands, you can also use [GitHub Desktop](https://desktop.github.com/), which is easier to get started. Go to [git documentation](https://git-scm.com/doc) if you want to really master git.
### Step 1: Fork the repository
1. Visit the project: <https://github.com/deepmodeling/deepmd-kit>
2. Click the **Fork** button on the top right and wait it to finish.
### Step 2: Clone the forked repository to local storage and set configurations
1. Clone your own repo, not the public repo (from deepmodeling) ! And change the branch to devel.
```bash
git clone https://github.com/$username/deepmd-kit.git
# Replace `$username` with your GitHub ID
git checkout devel
```
2. Add deepmodeling's repo as your remote repo, we can name it "upstream". And fetch upstream's latest codes to your workstation.
```bash
git remote add upstream https://github.com/deepmodeling/deepmd-kit.git
# After you add a remote repo, your local repo will be automatically named "origin".
git fetch upstream
# If your current codes are behind the latest codes, you should merge latest codes first.
# Notice you should merge from "devel"!
git merge upstream/devel
```
3. Modify your codes and design unit tests.
4. Commit your changes
```bash
git status # Checks the local status
git add <file> ... # Adds the file(s) you want to commit. If you want to commit all changes, you can directly use `git add.`
git commit -m "commit-message: update the xx"
```
5. Push the changed codes to your original repo on github.
```bash
git push origin devel
```
### Alternatively: Create a new branch
1. Get your local master up-to-date with upstream/master.
```bash
cd $working_dir/deepmd-kit
git fetch upstream
git checkout master
git rebase upstream/master
```
2. Create a new branch based on the master branch.
```bash
git checkout -b new-branch-name
```
3. Modify your codes and design unit tests.
4. Commit your changes
```bash
git status # Checks the local status
git add <file> ... # Adds the file(s) you want to commit. If you want to commit all changes, you can directly use `git add.`
git commit -m "commit-message: update the xx"
```
5. Keep your branch in sync with upstream/master
```bash
# While on your new branch
git fetch upstream
git rebase upstream/master
```
6. Push your changes to the remote
```bash
git push -u origin new-branch-name # "-u" is used to track the remote branch from origin
```
### Step 3: Create a pull request
1. Visit your fork at <https://github.com/$username/deepmd-kit> (replace `$username` with your GitHub ID)
2. Click `pull requests`, followed by `New pull request` and `Compare & pull request` to create your PR.
Now, your PR is successfully submitted! After this PR is merged, you will automatically become a contributor to DeePMD-kit.
## Contact us
E-mail: contact@deepmodeling.org
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<span style="font-size:larger;">DeePMD-kit Manual</span>
========
[![GitHub release](https://img.shields.io/github/release/deepmodeling/deepmd-kit.svg?maxAge=86400)](https://github.com/deepmodeling/deepmd-kit/releases)
[![doi:10.1016/j.cpc.2018.03.016](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2018.03.016-blue)](https://doi.org/10.1016/j.cpc.2020.107206)
![Citations](https://citations.njzjz.win/10.1016/j.cpc.2018.03.016)
[![offline packages](https://img.shields.io/github/downloads/deepmodeling/deepmd-kit/total?label=offline%20packages)](https://github.com/deepmodeling/deepmd-kit/releases)
[![conda install](https://img.shields.io/badge/downloads-9k%20total-green.svg?style=round-square&label=conda%20install)](https://anaconda.org/deepmodeling/deepmd-kit)
[![pip install](https://img.shields.io/pypi/dm/deepmd-kit?label=pip%20install)](https://pypi.org/project/deepmd-kit)
[![docker pull](https://img.shields.io/docker/pulls/deepmodeling/deepmd-kit)](https://hub.docker.com/r/deepmodeling/deepmd-kit)
[![Documentation Status](https://readthedocs.org/projects/deepmd/badge/)](https://deepmd.readthedocs.io/)
# Table of contents
- [About DeePMD-kit](#about-deepmd-kit)
- [Highlights in v2.0](#highlights-in-deepmd-kit-v2.0)
- [Highlighted features](#highlighted-features)
- [License and credits](#license-and-credits)
- [Deep Potential in a nutshell](#deep-potential-in-a-nutshell)
- [Download and install](#download-and-install)
- [Use DeePMD-kit](#use-deepmd-kit)
- [Code structure](#code-structure)
- [Troubleshooting](#troubleshooting)
# About DeePMD-kit
DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems.
For more information, check the [documentation](https://deepmd.readthedocs.io/).
# Highlights in DeePMD-kit v2.0
* [Model compression](doc/freeze/compress.md). Accelerate the efficiency of model inference for 4-15 times.
* [New descriptors](doc/model/overall.md). Including [`se_e2_r`](doc/model/train-se-e2-r.md) and [`se_e3`](doc/model/train-se-e3.md).
* [Hybridization of descriptors](doc/model/train-hybrid.md). Hybrid descriptor constructed from concatenation of several descriptors.
* [Atom type embedding](doc/model/train-se-e2-a-tebd.md). Enable atom type embedding to decline training complexity and refine performance.
* Training and inference the dipole (vector) and polarizability (matrix).
* Split of training and validation dataset.
* Optimized training on GPUs.
## Highlighted features
* **interfaced with TensorFlow**, one of the most popular deep learning frameworks, making the training process highly automatic and efficient, in addition Tensorboard can be used to visualize training procedure.
* **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, i.e., LAMMPS and i-PI, respectively.
* **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems including organic molecules, metals, semiconductors, and insulators, etc.
* **implements MPI and GPU supports**, makes it highly efficient for high performance parallel and distributed computing.
* **highly modularized**, easy to adapt to different descriptors for deep learning based potential energy models.
## License and credits
The project DeePMD-kit is licensed under [GNU LGPLv3.0](./LICENSE).
If you use this code in any future publications, please cite this using
``Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184.``
## Deep Potential in a nutshell
The goal of Deep Potential is to employ deep learning techniques and realize an inter-atomic potential energy model that is general, accurate, computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are arranged in a symmetry preserving way. These local coordinates are then transformed, through a sub-network, to a so-called *atomic energy*. Summing up all the atomic energies gives the potential energy of the system.
The initial proof of concept is in the [Deep Potential][1] paper, which employed an approach that was devised to train the neural network model with the potential energy only. With typical *ab initio* molecular dynamics (AIMD) datasets this is insufficient to reproduce the trajectories. The Deep Potential Molecular Dynamics ([DeePMD][2]) model overcomes this limitation. In addition, the learning process in DeePMD improves significantly over the Deep Potential method thanks to the introduction of a flexible family of loss functions. The NN potential constructed in this way reproduces accurately the AIMD trajectories, both classical and quantum (path integral), in extended and finite systems, at a cost that scales linearly with system size and is always several orders of magnitude lower than that of equivalent AIMD simulations.
Although being highly efficient, the original Deep Potential model satisfies the extensive and symmetry-invariant properties of a potential energy model at the price of introducing discontinuities in the model. This has negligible influence on a trajectory from canonical sampling but might not be sufficient for calculations of dynamical and mechanical properties. These points motivated us to develop the Deep Potential-Smooth Edition ([DeepPot-SE][3]) model, which replaces the non-smooth local frame with a smooth and adaptive embedding network. DeepPot-SE shows great ability in modeling many kinds of systems that are of interests in the fields of physics, chemistry, biology, and materials science.
In addition to building up potential energy models, DeePMD-kit can also be used to build up coarse-grained models. In these models, the quantity that we want to parameterize is the free energy, or the coarse-grained potential, of the coarse-grained particles. See the [DeePCG paper][4] for more details.
# Download and install
Please follow our [GitHub](https://github.com/deepmodeling/deepmd-kit) webpage to download the [latest released version](https://github.com/deepmodeling/deepmd-kit/tree/master) and [development version](https://github.com/deepmodeling/deepmd-kit/tree/devel).
DeePMD-kit offers multiple installation methods. It is recommend using easily methods like [offline packages](doc/install/easy-install.md#offline-packages), [conda](doc/install/easy-install.md#with-conda) and [docker](doc/install/easy-install.md#with-docker).
One may manually install DeePMD-kit by following the instuctions on [installing the Python interface](doc/install/install-from-source.md#install-the-python-interface) and [installing the C++ interface](doc/install/install-from-source.md#install-the-c-interface). The C++ interface is necessary when using DeePMD-kit with LAMMPS, i-PI or GROMACS.
# Use DeePMD-kit
A quick-start on using DeePMD-kit can be found as follows:
- [Prepare data with dpdata](doc/data/dpdata.md)
- [Training a model](doc/train/training.md)
- [Freeze a model](doc/freeze/freeze.md)
- [Test a model](doc/test/test.md)
- [Run MD with LAMMPS](doc/third-party/lammps.md)
A full [document](doc/train/train-input-auto.rst) on options in the training input script is available.
# Advanced
- [Installation](doc/install/index.md)
- [Easy install](doc/install/easy-install.md)
- [Install from source code](doc/install/install-from-source.md)
- [Install LAMMPS](doc/install/install-lammps.md)
- [Install i-PI](doc/install/install-ipi.md)
- [Install GROMACS](doc/install/install-gromacs.md)
- [Building conda packages](doc/install/build-conda.md)
- [Data](doc/data/index.md)
- [System](doc/data/system.md)
- [Formats of a system](doc/data/data-conv.md)
- [Prepare data with dpdata](doc/data/dpdata.md)
- [Model](doc/model/index.md)
- [Overall](doc/model/overall.md)
- [Descriptor `"se_e2_a"`](doc/model/train-se-e2-a.md)
- [Descriptor `"se_e2_r"`](doc/model/train-se-e2-r.md)
- [Descriptor `"se_e3"`](doc/model/train-se-e3.md)
- [Descriptor `"se_atten"`](doc/model/train-se-atten.md)
- [Descriptor `"hybrid"`](doc/model/train-hybrid.md)
- [Descriptor `sel`](doc/model/sel.md)
- [Fit energy](doc/model/train-energy.md)
- [Fit `tensor` like `Dipole` and `Polarizability`](doc/model/train-fitting-tensor.md)
- [Train a Deep Potential model using `type embedding` approach](doc/model/train-se-e2-a-tebd.md)
- [Deep potential long-range](doc/model/dplr.md)
- [Deep Potential - Range Correction (DPRc)](doc/model/dprc.md)
- [Training](doc/train/index.md)
- [Training a model](doc/train/training.md)
- [Advanced options](doc/train/training-advanced.md)
- [Parallel training](doc/train/parallel-training.md)
- [TensorBoard Usage](doc/train/tensorboard.md)
- [Known limitations of using GPUs](doc/train/gpu-limitations.md)
- [Training Parameters](doc/train/train-input-auto.rst)
- [Freeze and Compress](doc/freeze/index.rst)
- [Freeze a model](doc/freeze/freeze.md)
- [Compress a model](doc/freeze/compress.md)
- [Test](doc/test/index.rst)
- [Test a model](doc/test/test.md)
- [Calculate Model Deviation](doc/test/model-deviation.md)
- [Inference](doc/inference/index.rst)
- [Python interface](doc/inference/python.md)
- [C++ interface](doc/inference/cxx.md)
- [Integrate with third-party packages](doc/third-party/index.rst)
- [Use deep potential with ASE](doc/third-party/ase.md)
- [Run MD with LAMMPS](doc/third-party/lammps.md)
- [LAMMPS commands](doc/third-party/lammps-command.md)
- [Run path-integral MD with i-PI](doc/third-party/ipi.md)
- [Run MD with GROMACS](doc/third-party/gromacs.md)
- [Interfaces out of DeePMD-kit](doc/third-party/out-of-deepmd-kit.md)
- [Use NVNMD](doc/nvnmd/index.md)
# Code structure
The code is organized as follows:
* `data/raw`: tools manipulating the raw data files.
* `examples`: examples.
* `deepmd`: DeePMD-kit python modules.
* `source/api_cc`: source code of DeePMD-kit C++ API.
* `source/ipi`: source code of i-PI client.
* `source/lib`: source code of DeePMD-kit library.
* `source/lmp`: source code of Lammps module.
* `source/gmx`: source code of Gromacs plugin.
* `source/op`: tensorflow op implementation. working with library.
# Troubleshooting
- [Model compatibility](doc/troubleshooting/model_compatability.md)
- [Installation](doc/troubleshooting/installation.md)
- [The temperature undulates violently during early stages of MD](doc/troubleshooting/md_energy_undulation.md)
- [MD: cannot run LAMMPS after installing a new version of DeePMD-kit](doc/troubleshooting/md_version_compatibility.md)
- [Do we need to set rcut < half boxsize?](doc/troubleshooting/howtoset_rcut.md)
- [How to set sel?](doc/troubleshooting/howtoset_sel.md)
- [How to control the number of nodes used by a job?](doc/troubleshooting/howtoset_num_nodes.md)
- [How to tune Fitting/embedding-net size?](doc/troubleshooting/howtoset_netsize.md)
# Contributing
See [DeePMD-kit Contributing Guide](CONTRIBUTING.md) to become a contributor! 🤓
[1]: https://arxiv.org/abs/1707.01478
[2]: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.143001
[3]: https://arxiv.org/abs/1805.09003
[4]: https://aip.scitation.org/doi/full/10.1063/1.5027645
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Change Dir: /public/home/zhangqha/sourcecode/source/examples/TensorFlow/Train/new/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-GU1KWh
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src.c:(.text+0x4a): undefined reference to `pthread_detach'
src.c:(.text+0x56): undefined reference to `pthread_cancel'
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pthread_detach(thread);
pthread_cancel(thread);
pthread_join(thread, NULL);
pthread_atfork(NULL, NULL, NULL);
pthread_exit(NULL);
return 0;
}
Determining if the function pthread_create exists in the pthreads failed with the following output:
Change Dir: /public/home/zhangqha/sourcecode/source/examples/TensorFlow/Train/new/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-MeJDCi
Run Build Command(s):/tmp/pip-build-env-6r5leccz/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_e25c5 && [1/2] Building C object CMakeFiles/cmTC_e25c5.dir/CheckFunctionExists.c.o
[2/2] Linking C executable cmTC_e25c5
FAILED: cmTC_e25c5
: && /usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTC_e25c5.dir/CheckFunctionExists.c.o -o cmTC_e25c5 -lpthreads && :
/usr/bin/ld: cannot find -lpthreads
collect2: error: ld returned 1 exit status
ninja: build stopped: subcommand failed.
Performing C SOURCE FILE Test CMAKE_HAVE_LIBC_PTHREAD failed with the following output:
Change Dir: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-maaLfJ
Run Build Command(s):/tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_1daa7 && [1/2] Building C object CMakeFiles/cmTC_1daa7.dir/src.c.o
[2/2] Linking C executable cmTC_1daa7
FAILED: cmTC_1daa7
: && /usr/bin/cc CMakeFiles/cmTC_1daa7.dir/src.c.o -o cmTC_1daa7 && :
CMakeFiles/cmTC_1daa7.dir/src.c.o: In function `main':
src.c:(.text+0x3e): undefined reference to `pthread_create'
src.c:(.text+0x4a): undefined reference to `pthread_detach'
src.c:(.text+0x56): undefined reference to `pthread_cancel'
src.c:(.text+0x67): undefined reference to `pthread_join'
collect2: error: ld returned 1 exit status
ninja: build stopped: subcommand failed.
Source file was:
#include <pthread.h>
static void* test_func(void* data)
{
return data;
}
int main(void)
{
pthread_t thread;
pthread_create(&thread, NULL, test_func, NULL);
pthread_detach(thread);
pthread_cancel(thread);
pthread_join(thread, NULL);
pthread_atfork(NULL, NULL, NULL);
pthread_exit(NULL);
return 0;
}
Determining if the function pthread_create exists in the pthreads failed with the following output:
Change Dir: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-2YQCXJ
Run Build Command(s):/tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_9db68 && [1/2] Building C object CMakeFiles/cmTC_9db68.dir/CheckFunctionExists.c.o
[2/2] Linking C executable cmTC_9db68
FAILED: cmTC_9db68
: && /usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTC_9db68.dir/CheckFunctionExists.c.o -o cmTC_9db68 -lpthreads && :
/usr/bin/ld: cannot find -lpthreads
collect2: error: ld returned 1 exit status
ninja: build stopped: subcommand failed.
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