DeePMD-kit logo

DeePMD-kit

GitHub release offline packages conda-forge pip install docker pull Documentation Status

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.

Highlighted features

  • interfaced with multiple backends, including TensorFlow, PyTorch, and JAX, the most popular deep learning frameworks, making the training process highly automatic and efficient.
  • interfaced with high-performance classical MD and quantum (path-integral) MD packages, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABACUS.
  • 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. If you use this code in any future publications, please cite the following publications for general purpose:

  • 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. doi:10.1016/j.cpc.2018.03.016 Citations
  • Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik, Li'ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan, Sigbjørn Løland Bore, Chun Cai, Yinnian Lin, Bo Wang, Jiayan Xu, Jia-Xin Zhu, Chenxing Luo, Yuzhi Zhang, Rhys E. A. Goodall, Wenshuo Liang, Anurag Kumar Singh, Sikai Yao, Jingchao Zhang, Renata Wentzcovitch, Jiequn Han, Jie Liu, Weile Jia, Darrin M. York, Weinan E, Roberto Car, Linfeng Zhang, Han Wang. "DeePMD-kit v2: A software package for deep potential models." J. Chem. Phys. 159 (2023): 054801. doi:10.1063/5.0155600 Citations

In addition, please follow the bib file to cite the methods you used.

Highlights in major versions

Initial version

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 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) 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) 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 for more details.

v1

  • Code refactor to make it highly modularized.
  • GPU support for descriptors.

v2

  • Model compression. Accelerate the efficiency of model inference 4-15 times.
  • New descriptors. Including se_e2_r, se_e3, and se_atten (DPA-1).
  • Hybridization of descriptors. Hybrid descriptor constructed from the concatenation of several descriptors.
  • Atom type embedding. 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, including CUDA and ROCm.
  • Non-von-Neumann.
  • C API to interface with the third-party packages.

See our latest paper for details of all features until v2.2.3.

v3

  • Multiple backends supported. Add PyTorch and JAX backends.
  • The DPA-2 model.
  • Plugin mechanisms for external models.

Install and use DeePMD-kit

Please read the online documentation for how to install and use DeePMD-kit.

Code structure

The code is organized as follows:

  • examples: examples.
  • deepmd: DeePMD-kit python modules.
  • source/lib: source code of the core library.
  • source/op: Operator (OP) implementation.
  • source/api_cc: source code of DeePMD-kit C++ API.
  • source/api_c: source code of the C API.
  • source/nodejs: source code of the Node.js API.
  • source/ipi: source code of i-PI client.
  • source/lmp: source code of LAMMPS module.
  • source/gmx: source code of Gromacs plugin.

Contributing

See DeePMD-kit Contributing Guide to become a contributor! 🤓

Project links

Data verified by PyPI on Dec 23, 2024
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (3.2.0)

Owner

PyPI data
Data sourced directly from PyPI's database.

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for amcadmus from gravatar.com amcadmus

Credits

Author: DeepModeling

GitHub Statistics

Data verified by PyPI on Dec 23, 2024
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

GNU Lesser General Public License v3 (LGPLv3) (GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundati...)

Requires

Python >=3.9

Provides Extra

test docs lmp ipi gui cu11 cu12 jax cpu gpu mpi torch

Tags

deepmd

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

deepmd_kit-3.0.1.tar.gz (1.1 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

deepmd_kit-3.0.1-py37-none-win_amd64.whl (1.4 MB view details)

Uploaded Python 3.7Windows x86-64

deepmd_kit-3.0.1-py37-none-manylinux_2_28_x86_64.whl (17.4 MB view details)

Uploaded Python 3.7manylinux: glibc 2.28+ x86-64

deepmd_kit-3.0.1-py37-none-manylinux_2_28_aarch64.whl (12.4 MB view details)

Uploaded Python 3.7manylinux: glibc 2.28+ ARM64

deepmd_kit-3.0.1-py37-none-macosx_11_0_arm64.whl (10.6 MB view details)

Uploaded Python 3.7macOS 11.0+ ARM64

deepmd_kit-3.0.1-py37-none-macosx_10_13_x86_64.whl (17.5 MB view details)

Uploaded Python 3.7macOS 10.13+ x86-64

File details

Details for the file deepmd_kit-3.0.1.tar.gz.

File metadata

  • Download URL: deepmd_kit-3.0.1.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for deepmd_kit-3.0.1.tar.gz
Algorithm Hash digest
SHA256 10d4443c6fe31a9a4573ed6eda73b6a669dae572cf2bc43f45e9a63aaae02cff
MD5 c3da9e2556f476e813bcf446a6ba8f15
BLAKE2b-256 5cf53be09970408c89c7767ab629cf80ecbd984ef3401a534a41e332f0e75bb7

See more details on using hashes here.

Provenance

The following attestation bundles were made for deepmd_kit-3.0.1.tar.gz:

Publisher: build_wheel.yml on deepmodeling/deepmd-kit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file deepmd_kit-3.0.1-py37-none-win_amd64.whl.

File metadata

  • Download URL: deepmd_kit-3.0.1-py37-none-win_amd64.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: Python 3.7, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for deepmd_kit-3.0.1-py37-none-win_amd64.whl
Algorithm Hash digest
SHA256 9c395e1313a5ed7a77f60b743f7b43de80aee30f26e4101323e11189e8c0197d
MD5 136471d053e74ce3aafde8bd2a1cbd6b
BLAKE2b-256 cb4eb2b553312b7f052bf2e61c973d78e354ffa7f84e9bcac148fe55c002fcfd

See more details on using hashes here.

Provenance

The following attestation bundles were made for deepmd_kit-3.0.1-py37-none-win_amd64.whl:

Publisher: build_wheel.yml on deepmodeling/deepmd-kit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file deepmd_kit-3.0.1-py37-none-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for deepmd_kit-3.0.1-py37-none-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 90f312f31d77dbd576479c61c0333ee3ca5fa7be9984cb6c557e51fef4912fc8
MD5 54e28c2aa42b7830f98b52fbc9025580
BLAKE2b-256 062f9c7b1de6e337ad7b8b0baa967998e1d47342b2f1a6bd6f75552bc28bc9e4

See more details on using hashes here.

Provenance

The following attestation bundles were made for deepmd_kit-3.0.1-py37-none-manylinux_2_28_x86_64.whl:

Publisher: build_wheel.yml on deepmodeling/deepmd-kit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file deepmd_kit-3.0.1-py37-none-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for deepmd_kit-3.0.1-py37-none-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 baadc4c687d806dd37e66b776089e3ca24c809478ed0b496b9cdd6d480b853e8
MD5 3d9b195b92d497c3070ebd209a26293e
BLAKE2b-256 0fd6468c791f9a6949cb7f49fcabe0ba60ed769da141baec19bdc745b249e95b

See more details on using hashes here.

Provenance

The following attestation bundles were made for deepmd_kit-3.0.1-py37-none-manylinux_2_28_aarch64.whl:

Publisher: build_wheel.yml on deepmodeling/deepmd-kit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file deepmd_kit-3.0.1-py37-none-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for deepmd_kit-3.0.1-py37-none-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ac409ddeae85035803872ebebbd5b7f176577f7d86e72ca848e5296bfd1fc287
MD5 3498f72f9156ec9701e9770049ae30a6
BLAKE2b-256 1bd4642a75e1e9b342e595dc6c5f40941bc4f1df462185b6f6d6d3a08ab1f4b8

See more details on using hashes here.

Provenance

The following attestation bundles were made for deepmd_kit-3.0.1-py37-none-macosx_11_0_arm64.whl:

Publisher: build_wheel.yml on deepmodeling/deepmd-kit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file deepmd_kit-3.0.1-py37-none-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for deepmd_kit-3.0.1-py37-none-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 e7f11080631c27097f26a7bfcf62c7f2555d3360a0f675fa5092d6a8fc11efa2
MD5 a7fee48b042625cca76e0a5461f7cc6f
BLAKE2b-256 e78dc1aa7365bffffa3aaa10a519f303256773a1e6749ed21533bf7e67b59691

See more details on using hashes here.

Provenance

The following attestation bundles were made for deepmd_kit-3.0.1-py37-none-macosx_10_13_x86_64.whl:

Publisher: build_wheel.yml on deepmodeling/deepmd-kit

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

3.2.0

6 files

Pre-release

3.2.0b0

6 files

3.1.3

6 files

3.1.2

6 files

3.1.1

6 files

3.1.0

6 files

Pre-release

3.1.0rc0

6 files

Pre-release

3.1.0a0

6 files

3.0.3

6 files

3.0.2

6 files

This release

3.0.1 This release

6 files

3.0.0

6 files

Pre-release

3.0.0rc0

6 files

Pre-release

3.0.0b4

6 files

Pre-release

3.0.0b3

6 files

Pre-release

3.0.0b2

6 files

Pre-release

3.0.0b1

6 files

Pre-release

3.0.0b0

6 files

Pre-release

3.0.0a0

6 files

2.2.11

6 files

2.2.10

6 files

2.2.9

6 files

2.2.8

6 files

2.2.7

6 files

2.2.6

6 files

2.2.5

6 files

2.2.4

6 files

2.2.3

6 files

2.2.2

5 files

2.2.1

5 files

2.2.0

5 files

Pre-release

2.2.0b0

5 files

2.1.5

6 files

2.1.4

6 files

2.1.3

6 files

2.1.2

1 file

2.1.1

6 files

2.1.0

6 files

2.0.3

5 files

2.0.2

5 files

2.0.1

5 files

2.0.0

5 files

Pre-release

2.0.0b4

5 files

Pre-release

2.0.0b3

4 files

Pre-release

2.0.0b2

4 files

Pre-release

2.0.0b1

3 files

Pre-release

2.0.0b0

4 files

Pre-release

2.0.0a1

Pre-release

2.0.0a0

4 files

1.3.3

4 files

1.3.2

4 files

1.3.1

4 files

1.3.0

4 files

1.2.4

4 files

1.2.3

4 files

1.2.2

4 files

1.2.1

3 files

1.2.0

3 files

1.1.5

4 files

1.1.4

3 files

1.1.3

3 files

1.1.2

3 files

1.1.1

4 files

1.1.0

4 files

1.0.2

4 files

1.0.1

4 files

1.0.0

4 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page