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nequip

NequIP is a code for building E(3)-equivariant interatomic potentials

https://github.com/mir-group/nequip

Science Score: 85.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org, nature.com, acm.org
  • ✓
    Committers with academic emails
    5 of 22 committers (22.7%) from academic institutions
  • ✓
    Institutional organization owner
    Organization mir-group has institutional domain (bkoz.seas.harvard.edu)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.0%) to scientific vocabulary

Keywords

atomistic-simulations computational-chemistry deep-learning drug-discovery force-fields interatomic-potentials machine-learning materials-science molecular-dynamics pytorch
Last synced: 6 months ago · JSON representation ·

Repository

NequIP is a code for building E(3)-equivariant interatomic potentials

Basic Info
Statistics
  • Stars: 768
  • Watchers: 21
  • Forks: 170
  • Open Issues: 6
  • Releases: 29
Topics
atomistic-simulations computational-chemistry deep-learning drug-discovery force-fields interatomic-potentials machine-learning materials-science molecular-dynamics pytorch
Created almost 5 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License Citation Authors

README.md

NequIP


Documentation Status PyPI version

NequIP

NequIP is an open-source code for building E(3)-equivariant interatomic potentials.

[!IMPORTANT] A major backwards-incompatible update to the nequip package was released on April 23rd 2025 as version v0.7.0. The previous version v0.6.2 can still be found for use with existing config files in the GitHub Releases and on PyPI.

Installation and usage

Installation instructions and user guides can be found in our docs.

Tutorial

The best way to learn how to use NequIP is through the tutorial notebook. This will run entirely on Google Colab's cloud virtual machine; you do not need to install or run anything locally.

Highlighted Features

The following are some notable features, with quick links for more details:

Extension Packages

The NequIP software framework is designed to be flexible and extensible: you can build custom architectures, implement new training techniques, and develop additional methods on top of it through extension packages. If you're interested in developing your own extension package, please refer to the extension package docs and consider joining our Zulip for developer-focused discussions and collaborations.

Notable examples of NequIP framework extension packages include

  • Allegro (GitHub, Docs, Paper):
    Strictly local equivariant models with excellent scalability for multirank molecular dynamics simulations.
  • NequIP-LES (GitHub, Paper):
    An extension of NequIP and Allegro that adds long-range electrostatics via the Latent Ewald Summation (LES) algorithm.

References & citing

Any and all use of this software, in whole or in part, should clearly acknowledge and link to this repository.

If you use this code in your academic work, please cite:

  1. The preprint describing the NequIP software framework: > Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak, Gabriel de Miranda Nascimento, Seán R. Kavanagh, Laura Zichi, Menghang Wang, Aadit Saluja, Yizhong R. Hu, Tess Smidt, Anders Johansson, William C. Witt, Boris Kozinsky, Albert Musaelian.
    > "High-performance training and inference for deep equivariant interatomic potentials."
    > https://doi.org/10.48550/arXiv.2504.16068

And also consider citing:

  1. The original NequIP paper

    Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky.
    "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials."
    Nature communications 13, no. 1 (2022): 2453

  2. The computational scaling paper that discusses optimized LAMMPS MD

    Albert Musaelian, Anders Johansson, Simon Batzner, and Boris Kozinsky.
    "Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size."
    In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-12. 2023.

  3. The e3nn equivariant neural network package used by NequIP, through its preprint and/or code

Extension packages like Allegro have their own additional relevant citations.

BibTeX entries for a number of the relevant papers are provided for convenience in CITATION.bib.

Authors

Please see AUTHORS.md.

Community, contact, questions, and contributing

If you find a bug or have a proposal for a feature, please post it in the Issues. If you have a self-contained question or other discussion topic, try our GitHub Discussions.

Active users and interested developers are invited to join us on the NequIP community chat server, which is hosted on the excellent Zulip software. Zulip is organized a little bit differently than chat software like Slack or Discord that you may be familiar with: please review their introduction before posting. Fill out the interest form for the NequIP community here.

If you want to contribute to the code, please read "Contributing to NequIP".

We can also be reached by email at allegro-nequip@g.harvard.edu.

Owner

  • Name: MIR@Harvard
  • Login: mir-group
  • Kind: organization

Materials Intelligence Group @ Harvard University

Citation (CITATION.bib) 

@article{tan2025high,
  title={High-performance training and inference for deep equivariant interatomic potentials},
  author={Tan, Chuin Wei and Descoteaux, Marc L and Kotak, Mit and Nascimento, Gabriel de Miranda and Kavanagh, Se{\'a}n R and Zichi, Laura and Wang, Menghang and Saluja, Aadit and Hu, Yizhong R and Smidt, Tess and Johansson, Anders and Witt, William C. and Kozinsky, Boris and Musaelian, Albert},
  journal={arXiv preprint arXiv:2504.16068},
  year={2025}
}
@article{batzner20223,
  title={E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials},
  author={Batzner, Simon and Musaelian, Albert and Sun, Lixin and Geiger, Mario and Mailoa, Jonathan P and Kornbluth, Mordechai and Molinari, Nicola and Smidt, Tess E and Kozinsky, Boris},
  journal={Nature communications},
  volume={13},
  number={1},
  pages={2453},
  year={2022},
  publisher={Nature Publishing Group UK London}
}
@inproceedings{kozinsky2023scaling,
  title={Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size},
  author={Kozinsky, Boris and Musaelian, Albert and Johansson, Anders and Batzner, Simon},
  booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  pages={1--12},
  year={2023}
}
@article{musaelian2023learning,
  title={Learning local equivariant representations for large-scale atomistic dynamics},
  author={Musaelian, Albert and Batzner, Simon and Johansson, Anders and Sun, Lixin and Owen, Cameron J and Kornbluth, Mordechai and Kozinsky, Boris},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={579},
  year={2023},
  publisher={Nature Publishing Group UK London}
}
@article{zhu2023fast,
  title={Fast uncertainty estimates in deep learning interatomic potentials},
  author={Zhu, Albert and Batzner, Simon and Musaelian, Albert and Kozinsky, Boris},
  journal={The Journal of Chemical Physics},
  volume={158},
  number={16},
  year={2023},
  publisher={AIP Publishing}
}
@article{geiger2022e3nn,
  title={e3nn: Euclidean neural networks},
  author={Geiger, Mario and Smidt, Tess},
  journal={arXiv preprint arXiv:2207.09453},
  year={2022}
}

GitHub Events

Total
  • Create event: 21
  • Release event: 10
  • Issues event: 40
  • Watch event: 145
  • Delete event: 30
  • Issue comment event: 72
  • Push event: 83
  • Pull request review comment event: 36
  • Pull request review event: 42
  • Pull request event: 43
  • Fork event: 37
Last Year
  • Create event: 21
  • Release event: 10
  • Issues event: 40
  • Watch event: 145
  • Delete event: 30
  • Issue comment event: 72
  • Push event: 83
  • Pull request review comment event: 36
  • Pull request review event: 42
  • Pull request event: 43
  • Fork event: 37

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 2,473
  • Total Committers: 22
  • Avg Commits per committer: 112.409
  • Development Distribution Score (DDS): 0.582
Past Year
  • Commits: 949
  • Committers: 14
  • Avg Commits per committer: 67.786
  • Development Distribution Score (DDS): 0.149
Top Committers
Name Email Commits
Linux-cpp-lisp 1****p 1,033
cw-tan c****n@g****m 814
nw13slx n****o@g****m 430
Simon Batzner s****r@g****m 77
Sean Kavanagh s****9@i****k 63
Albert Zhu a****u@c****u 12
Gabriel de Miranda d****o@g****m 9
Yizhong Richard Hu r****1@g****m 8
Vivek Bharadwaj v****j@b****u 5
Fabian 4****Z 5
Marc Descoteaux 8****3 4
Lixin Sun l****n@m****m 2
laurazichi l****i@g****u 2
anjohan a****s@s****v 1
Eike 7****d 1
Laura Zichi 5****i 1
Marcel Langer me@s****m 1
Myles Stapelberg 3****g 1
Peter Eastman p****n@g****m 1
Olav Forland o****o@l****v 1
Albert Zhu a****u@h****u 1
VIRAJ SINGH v****h@g****m 1
Committer Domains (Top 20 + Academic)
holy2c02301.rc.fas.harvard.edu: 1 login28.chn.perlmutter.nersc.gov: 1 sirmarcel.com: 1 sandia.gov: 1 g.harvard.edu: 1 microsoft.com: 1 berkeley.edu: 1 college.harvard.edu: 1 imperial.ac.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 98
  • Total pull requests: 112
  • Average time to close issues: 6 months
  • Average time to close pull requests: 3 months
  • Total issue authors: 76
  • Total pull request authors: 24
  • Average comments per issue: 4.1
  • Average comments per pull request: 1.18
  • Merged pull requests: 79
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 16
  • Pull requests: 44
  • Average time to close issues: 2 months
  • Average time to close pull requests: 24 days
  • Issue authors: 12
  • Pull request authors: 13
  • Average comments per issue: 1.88
  • Average comments per pull request: 0.7
  • Merged pull requests: 23
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • kavanase (5)
  • peastman (3)
  • schiotz (3)
  • tgmaxson (3)
  • utkarshp1161 (2)
  • mkphuthi (2)
  • keano130 (2)
  • svandenhaute (2)
  • miroi (2)
  • fxcoudert (2)
  • QuantumMisaka (2)
  • emilannevelink (2)
  • PythonFZ (2)
  • knc6 (2)
  • deleep225 (2)
Pull Request Authors
  • Linux-cpp-lisp (37)
  • kavanase (17)
  • nw13slx (16)
  • cw-tan (13)
  • simonbatzner (7)
  • mstapelberg (6)
  • vbharadwaj-bk (4)
  • tjgiese (4)
  • bastonero (2)
  • frobnitzem (2)
  • ESEberhard (2)
  • MarcD3 (2)
  • anjohan (2)
  • chirag1701 (2)
  • peastman (1)
Top Labels
Issue Labels
question (38) bug (31) enhancement (19)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 151,040 last-month
  • Total docker downloads: 104
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 7
    (may contain duplicates)
  • Total versions: 20
  • Total maintainers: 3
pypi.org: nequip

NequIP is an open-source code for building E(3)-equivariant interatomic potentials.

  • Homepage: https://github.com/mir-group/nequip
  • Documentation: https://nequip.readthedocs.io/
  • License: MIT License Copyright (c) 2021 The President and Fellows of Harvard College Copyright (c) 2025 The NequIP Developers Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  • Latest release: 0.13.1
    published 6 months ago
  • Versions: 18
  • Dependent Packages: 1
  • Dependent Repositories: 7
  • Downloads: 151,040 Last month
  • Docker Downloads: 104
Rankings
Docker downloads count: 3.2%
Dependent packages count: 4.7%
Dependent repos count: 5.6%
Average: 5.8%
Downloads: 9.5%
Maintainers (3)
albym cw-tan wcwitt
Last synced: 6 months ago
conda-forge.org: nequip
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Stargazers count: 21.2%
Forks count: 22.4%
Average: 32.2%
Dependent repos count: 34.0%
Dependent packages count: 51.2%
Last synced: 6 months ago

Dependencies

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setup.py pypi
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  • contextlib2 *
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  • e3nn >=0.4.4,<0.6.0
  • numpy *
  • pyyaml *
  • torch >=1.10.0,<1.13,
  • torch-ema >=0.3.0
  • torch-runstats >=0.2.0
  • tqdm *
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