https://github.com/mir-group/allegro
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓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
1 of 7 committers (14.3%) from academic institutions -
✓Institutional organization owner
Organization mir-group has institutional domain (bkoz.seas.harvard.edu) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (17.7%) to scientific vocabulary
Keywords
Repository
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Basic Info
- Host: GitHub
- Owner: mir-group
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://nequip.readthedocs.io/projects/allegro
- Size: 554 KB
Statistics
- Stars: 427
- Watchers: 20
- Forks: 67
- Open Issues: 7
- Releases: 10
Topics
Metadata Files
README.md
Allegro
This package implements the Allegro E(3)-equivariant machine learning interatomic potential.
In particular, allegro implements the Allegro model as an extension package for the NequIP framework.
- Installation
- Usage
- LAMMPS Integration
- References & citing
- Community, contact, questions, and contributing
[!IMPORTANT] A major backwards-incompatible update to the
nequipframework was released on April 23rd 2025 as version v0.7.0. The correspondingallegroversion is v0.4.0. Previous versions of Allegro remain available if needed in the GitHub Releases and must be used with older versions ofnequip.
Installation
allegro requires the nequip package. Details on nequip and its required PyTorch versions can be found in the nequip docs.
allegro can be installed from PyPI (note that it is known as nequip-allegro on PyPI):
bash
pip install nequip-allegro
Installing allegro in this way will also install the nequip package from PyPI.
Usage
The allegro package provides the Allegro model for use within the NequIP framework.
The framework's documentation describes how to train, test, and use models.
A minimal example of a config file for training an Allegro model is provided at configs/tutorial.yaml and further details can be found in the Allegro docs.
LAMMPS Integration
We offer a LAMMPS plugin pair_allegro to use Allegro models in LAMMPS simulations, including support for Kokkos acceleration, MPI, and parallel multi-GPU simulations.
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:
The Allegro paper
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, and Boris Kozinsky.
"Learning local equivariant representations for large-scale atomistic dynamics."
Nature Communications 14, no. 1 (2023): 579The preprint describing the NequIP software framework and Allegro's performance within it
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.2504.16068The 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.
And also consider citing:
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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 The
e3nnequivariant neural network package used by NequIP, through its preprint and/or code
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 Disucssions.
If your post is related to the NequIP software framework in general, please post in the issues or discussions on that repository. Discussions on this repository should be specific to the allegro package and Allegro model.
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.md from the nequip repository; this repository follows the same processes.
We can also be reached by email at allegro-nequip@g.harvard.edu.
Owner
- Name: MIR@Harvard
- Login: mir-group
- Kind: organization
- Website: http://bkoz.seas.harvard.edu
- Twitter: Materials_Intel
- Repositories: 16
- Profile: https://github.com/mir-group
Materials Intelligence Group @ Harvard University
GitHub Events
Total
- Create event: 13
- Release event: 7
- Issues event: 37
- Watch event: 77
- Delete event: 7
- Issue comment event: 47
- Push event: 43
- Pull request review comment event: 5
- Pull request review event: 5
- Pull request event: 10
- Fork event: 15
Last Year
- Create event: 13
- Release event: 7
- Issues event: 37
- Watch event: 77
- Delete event: 7
- Issue comment event: 47
- Push event: 43
- Pull request review comment event: 5
- Pull request review event: 5
- Pull request event: 10
- Fork event: 15
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| cw-tan | c****n@g****m | 179 |
| Linux-cpp-lisp | 1****p | 110 |
| Simon Batzner | s****r@g****m | 14 |
| AaditSaluja | a****a@g****m | 2 |
| Mit Kotak | m****5@g****m | 1 |
| Chuin Wei Tan | c****n@b****u | 1 |
| AM | am | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 27
- Total pull requests: 11
- Average time to close issues: 11 months
- Average time to close pull requests: 6 days
- Total issue authors: 22
- Total pull request authors: 5
- Average comments per issue: 1.07
- Average comments per pull request: 0.09
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 13
- Pull requests: 7
- Average time to close issues: 7 days
- Average time to close pull requests: 2 days
- Issue authors: 10
- Pull request authors: 3
- Average comments per issue: 0.15
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- jonathan-booth (2)
- gcassone-cnr (2)
- kavanase (2)
- sogenyi (2)
- nec4 (2)
- haihai-00 (1)
- jimmysue (1)
- utkarshp1161 (1)
- tanamikan (1)
- yzjin (1)
- devireddyprasanth22 (1)
- liuyenfu (1)
- JSLJ23 (1)
- potus28 (1)
- xavierholt (1)
Pull Request Authors
- cw-tan (5)
- tjgiese (3)
- lyncdw19 (1)
- GengSS (1)
- kavanase (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 216,786 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 10
- Total maintainers: 3
pypi.org: nequip-allegro
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials.
- Documentation: https://nequip-allegro.readthedocs.io/
- License: MIT License Copyright (c) 2022 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.
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Latest release: 0.7.1
published 7 months ago
Rankings
Dependencies
- nequip >=0.5.3