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deepmd-kit-2.2.1
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مشاهده بیشترتوضیحات
A deep learning package for many-body potential energy representation and molecular dynamics
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepmd-kit-2.2.1 |
| نام | deepmd-kit |
| نسخه کتابخانه | 2.2.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | DeepModeling |
| ایمیل نویسنده | Han Wang <wang_han@iapcm.ac.cn> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/deepmd-kit/ |
| مجوز | GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. This version of the GNU Lesser General Public License incorporates the terms and conditions of version 3 of the GNU General Public License, supplemented by the additional permissions listed below. 0. Additional Definitions. As used herein, "this License" refers to version 3 of the GNU Lesser General Public License, and the "GNU GPL" refers to version 3 of the GNU General Public License. "The Library" refers to a covered work governed by this License, other than an Application or a Combined Work as defined below. An "Application" is any work that makes use of an interface provided by the Library, but which is not otherwise based on the Library. Defining a subclass of a class defined by the Library is deemed a mode of using an interface provided by the Library. A "Combined Work" is a work produced by combining or linking an Application with the Library. The particular version of the Library with which the Combined Work was made is also called the "Linked Version". The "Minimal Corresponding Source" for a Combined Work means the Corresponding Source for the Combined Work, excluding any source code for portions of the Combined Work that, considered in isolation, are based on the Application, and not on the Linked Version. The "Corresponding Application Code" for a Combined Work means the object code and/or source code for the Application, including any data and utility programs needed for reproducing the Combined Work from the Application, but excluding the System Libraries of the Combined Work. 1. Exception to Section 3 of the GNU GPL. You may convey a covered work under sections 3 and 4 of this License without being bound by section 3 of the GNU GPL. 2. Conveying Modified Versions. If you modify a copy of the Library, and, in your modifications, a facility refers to a function or data to be supplied by an Application that uses the facility (other than as an argument passed when the facility is invoked), then you may convey a copy of the modified version: a) under this License, provided that you make a good faith effort to ensure that, in the event an Application does not supply the function or data, the facility still operates, and performs whatever part of its purpose remains meaningful, or b) under the GNU GPL, with none of the additional permissions of this License applicable to that copy. 3. Object Code Incorporating Material from Library Header Files. The object code form of an Application may incorporate material from a header file that is part of the Library. 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[<picture><source media="(prefers-color-scheme: dark)" srcset="./doc/_static/logo-dark.svg"><source media="(prefers-color-scheme: light)" srcset="./doc/_static/logo.svg"><img alt="DeePMD-kit logo" src="./doc/_static/logo.svg"></picture>](./doc/logo.md)
--------------------------------------------------------------------------------
<span style="font-size:larger;">DeePMD-kit Manual</span>
========
[](https://github.com/deepmodeling/deepmd-kit/releases)
[](https://doi.org/10.1016/j.cpc.2020.107206)
[](https://badge.dimensions.ai/details/doi/10.1016/j.cpc.2018.03.016)
[](https://github.com/deepmodeling/deepmd-kit/releases)
[](https://anaconda.org/conda-forge/deepmd-kit)
[](https://pypi.org/project/deepmd-kit)
[](https://hub.docker.com/r/deepmodeling/deepmd-kit)
[](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 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 the 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 of 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 procedures.
* **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, insulators, etc.
* **implements MPI and GPU supports**, making 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 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 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 interest 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 recommended to use easy 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 instructions 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)
- [Multi-task training](doc/train/multi-task-training.md)
- [TensorBoard Usage](doc/train/tensorboard.md)
- [Known limitations of using GPUs](doc/train/gpu-limitations.md)
- [Training Parameters](doc/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 the library.
# Troubleshooting
- [Model compatibility](doc/troubleshooting/model_compatability.md)
- [Installation](doc/troubleshooting/installation.md)
- [The temperature undulates violently during the 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 parallelism of a job?](doc/troubleshooting/howtoset_num_nodes.md)
- [How to tune Fitting/embedding-net size?](doc/troubleshooting/howtoset_netsize.md)
- [Why does a model have low precision?](doc/troubleshooting/precision.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
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | scipy |
| - | pyyaml |
| >=0.2.9 | dargs |
| >=1.21 | python-hostlist |
| - | h5py |
| - | wcmatch |
| - | packaging |
| - | typing-extensions |
| >=1.4 | importlib-metadata |
| ==2.11.0 | tensorflow-cpu |
| ==2.11.0 | tensorflow |
| - | nvidia-cuda-runtime-cu11 |
| - | nvidia-cublas-cu11 |
| - | nvidia-cufft-cu11 |
| - | nvidia-curand-cu11 |
| - | nvidia-cusolver-cu11 |
| - | nvidia-cusparse-cu11 |
| - | nvidia-cudnn-cu11 |
| - | nvidia-cuda-runtime-cu12 |
| - | nvidia-cublas-cu12 |
| - | nvidia-cufft-cu12 |
| - | nvidia-curand-cu12 |
| - | nvidia-cusolver-cu12 |
| - | nvidia-cusparse-cu12 |
| - | nvidia-cudnn-cu12 |
| >=3.1.1 | sphinx |
| - | recommonmark |
| >=1.0.0rc1 | sphinx-rtd-theme |
| - | sphinx-markdown-tables |
| - | myst-parser |
| - | breathe |
| - | exhale |
| - | numpydoc |
| - | ase |
| >=0.1.0 | deepmodeling-sphinx |
| >=0.3.4 | dargs |
| - | sphinx-argparse |
| - | pygments-lammps |
| ==2.11.0 | tensorflow |
| ==2.11.0 | tensorflow |
| - | i-PI |
| - | find-libpython |
| - | find-libpython |
| ~=2022.6.23.3.0 | lammps |
| ~=2022.6.23.3.0 | lammps-manylinux-2-28 |
| >=0.1.9 | dpdata |
| - | ase |
| - | pytest |
| - | pytest-cov |
| - | pytest-sugar |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl deepmd-kit-2.2.1:
pip install deepmd-kit-2.2.1.whl
نصب پکیج tar.gz deepmd-kit-2.2.1:
pip install deepmd-kit-2.2.1.tar.gz