Data

Tools

Indexes

Applications

Experiments

Open Source Science

AniSOAP

AniSOAP: Machine Learning Representations for Coarse-grained and Non-spherical Systems - Published in JOSS (2025)

https://github.com/cersonsky-lab/anisoap

Science Score: 98.0%

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 1 DOI reference(s) in JOSS metadata
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Scientific Fields

Mathematics Computer Science - 84% confidence
Last synced: 6 months ago · JSON representation ·

Repository

Library for computing anisotropy extension to SOAP descriptors

Basic Info
Statistics
  • Stars: 11
  • Watchers: 3
  • Forks: 1
  • Open Issues: 6
  • Releases: 1
Created over 3 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

AniSOAP

AniSOAP is a Python library for creating descriptors of chemical systems suitable for machine learning use. This project aims to extend the popular Smooth Overlap of Atomic Positions (SOAP) descriptors to coarse-grained systems consisting of aspherical particles with anisotropic interactions.

Warning!

While technically complete, AniSOAP is in beta mode, and subject to new changes regularly. Please use with caution as we iron out some of the finer details.

Documentation

Please read our latest documentation, containing examples and API usage here: https://anisoap.readthedocs.io/en/latest/

Installation

AniSOAP requires the Rust language. If you do not already have Rust installed, we recommend using the rustup tool, available here. To check that Rust is installed correctly, enter rustc --version in a command prompt and make sure it does not return an error.

The installation of the library for python use can be done simply with

pip install .

which installs all of AniSOAP's dependencies and AniSOAP itself. This installs the latest version of each dependency. If these results in conflict, you can use

pip install -r requirements.txt

to install all the dependencies with frozen versions, followed by pip install . to install AniSOAP itself. You can test the library itself using

pytest tests/.

Please contact the developers if some tests fail.

For developers:

Contributions are welcome! For more information, please see the guidelines.

Contributors

Thanks goes to all people that make AniSOAP possible:

Owner

  • Name: The Cersonsky Lab at UW - Madison
  • Login: cersonsky-lab
  • Kind: organization
  • Email: rose.cersonsky@wisc.edu
  • Location: United States of America

JOSS Publication

AniSOAP: Machine Learning Representations for Coarse-grained and Non-spherical Systems
Published
July 10, 2025
DOI
Volume 10, Issue 111, Page 7954
Authors
Arthur Yan Lin ORCID
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA
Lucas Ortengren ORCID
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA
Seonwoo Hwang
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA
Yong-Cheol Cho ORCID
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA
Jigyasa Nigam ORCID
Research Laboratory of Electronics, Massachusetts Institute of Technology, USA
Rose K. Cersonsky ORCID
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA
Editor
Monica Bobra ORCID
Tags
machine learning molecular simulation
View PDF Review Thread Software Archive

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: AniSOAP
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Arthur
    family-names: Lin
    email: alin62@wisc.edu
    affiliation: University of Wisconsin-Madison
    orcid: 'https://orcid.org/0000-0002-7665-3767'
  - given-names: Lucas
    family-names: Ortengren
    orcid: 'https://orcid.org/0009-0002-8899-7513'
    affiliation: University of Wisconsin-Madison
    email: ortengren@wisc.edu
  - given-names: Seonwoo
    family-names: Hwang
    email: hwangsean1119@gmail.com
    affiliation: University of Wisconsin-Madison
  - given-names: Yong-Cheol
    family-names: Cho
    affiliation: University of Illinois Urbana-Champaign
    orcid: 'https://orcid.org/0009-0001-6038-6764'
  - given-names: Jigyasa
    family-names: Nigam
    orcid: 'https://orcid.org/0000-0001-6857-4332'
  - given-names: Rose
    family-names: Cersonsky
    email: rose.cersonsky@wisc.edu
    affiliation: University of Wisconsin-Madison
    orcid: 'https://orcid.org/0000-0003-4515-3441'
identifiers:
  - type: url
    value: 'https://github.com/cersonsky-lab/AniSOAP'
repository-code: 'https://github.com/cersonsky-lab/AniSOAP'
url: 'https://anisoap.readthedocs.io/en/latest/'
abstract: >-
  `AniSOAP` is a package that creates Machine Learning (ML)
  representations of non-spherical particle configurations;
  these representations can then be used in ML-driven
  simulations and analyses. This generalization of existing
  spherical ML representations therefore aims to bridge the
  gap between two scientific communities: The
  machine-learned atomistic simulation community, whose
  primary concern is obtaining fast and (quantum) accurate
  descriptions of the complex interactions occuring between
  (spherical) atoms, and the coarse-grained and colloid
  modeling community, whose primary concern is understanding
  emergent behavior of macroscopic particles with
  (plausibly) complex geometries. `AniSOAP` provide a common
  framework to answer scientific questions at the
  intersection of these two fields.
keywords:
  - Machine Learning
  - Anisotropy
  - Machine Learning Potentials
  - Molecular Simulation
license: Apache-2.0

GitHub Events

Total
  • Create event: 20
  • Release event: 1
  • Issues event: 26
  • Watch event: 4
  • Delete event: 10
  • Issue comment event: 31
  • Push event: 138
  • Pull request event: 24
  • Pull request review comment event: 27
  • Pull request review event: 26
Last Year
  • Create event: 20
  • Release event: 1
  • Issues event: 26
  • Watch event: 4
  • Delete event: 10
  • Issue comment event: 31
  • Push event: 138
  • Pull request event: 24
  • Pull request review comment event: 27
  • Pull request review event: 26

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 14
  • Total pull requests: 13
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 11 days
  • Total issue authors: 5
  • Total pull request authors: 3
  • Average comments per issue: 0.43
  • Average comments per pull request: 0.77
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 14
  • Pull requests: 13
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 11 days
  • Issue authors: 5
  • Pull request authors: 3
  • Average comments per issue: 0.43
  • Average comments per pull request: 0.77
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • arthur-lin1027 (8)
  • DaniBodor (6)
  • ortengren (3)
  • rosecers (1)
  • SamTov (1)
Pull Request Authors
  • arthur-lin1027 (14)
  • rosecers (6)
  • ortengren (5)
  • SeonwooH (3)
  • YCC-ProjBackups (2)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 22 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
pypi.org: anisoap

A package for computing anisotropic extensions to the SOAP formalism

  • Documentation: https://anisoap.readthedocs.io/
  • License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
  • Latest release: 0.1.1
    published 8 months ago
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 22 Last month
Rankings
Dependent packages count: 8.9%
Average: 29.5%
Dependent repos count: 50.1%
Maintainers (1)
rosecers
Last synced: 6 months ago

Dependencies

.github/workflows/lint.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/tests.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v1 composite
tests/requirements.txt pypi
  • coverage * test
  • numpy * test
  • pytest * test
  • scipy * test
pyproject.toml pypi
setup.py pypi