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aimmd

aimmd (AI for Molecular Mechanism Discovery) autonomously steers (a large number of) molecular dynamics simulations to efficiently sample and understand rare transition events.

https://github.com/bio-phys/aimmd

Science Score: 44.0%

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
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.8%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

aimmd (AI for Molecular Mechanism Discovery) autonomously steers (a large number of) molecular dynamics simulations to efficiently sample and understand rare transition events.

Basic Info
  • Host: GitHub
  • Owner: bio-phys
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 28.9 MB
Statistics
  • Stars: 18
  • Watchers: 3
  • Forks: 2
  • Open Issues: 0
  • Releases: 2
Created about 3 years ago · Last pushed 7 months ago
Metadata Files
Readme Changelog License Citation

README.md

aimmd

codecov Documentation Status PyPI

aimmd (AI for Molecular Mechanism Discovery) autonomously steers (a large number of) molecular dynamics simulations to efficiently sample and understand rare transition events.

Installation

Installing aimmd from PyPi is as easy as:

bash pip install aimmd

For more see the documentation.

Documentation and Code Examples

Please see the documentation for more information on aimmd and/or the jupyter notebooks in the examples folder for code examples.

Contributing

All contributions are appreciated! Please refer to the documentation for information.

This README.md is printed from 100% recycled electrons.

Owner

  • Name: bio-phys
  • Login: bio-phys
  • Kind: organization

Citation (CITATIONS.md) 

If you use aimmd in published work please cite:

- H. Jung, R. Covino, A. Arjun, C. Leitold, C. Dellago, P.G. Bolhuis and G. Hummer. Machine-guided path sampling to discover mechanisms of molecular self-organization. Nature Computational Science 3, 334–345 (2023). doi:[10.1038/s43588-023-00428-z](https://doi.org/10.1038/s43588-023-00428-z)

GitHub Events

Total
  • Release event: 2
  • Watch event: 8
  • Delete event: 3
  • Issue comment event: 4
  • Push event: 13
  • Pull request event: 17
  • Create event: 6
Last Year
  • Release event: 2
  • Watch event: 8
  • Delete event: 3
  • Issue comment event: 4
  • Push event: 13
  • Pull request event: 17
  • Create event: 6

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 0
  • Total pull requests: 11
  • Average time to close issues: N/A
  • Average time to close pull requests: 23 days
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.45
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 10
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 day
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.4
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • hejung (2)
  • clement-wespiser (1)
  • s-schaef (1)
Pull Request Authors
  • hejung (16)
Top Labels
Issue Labels
enhancement (2) documentation (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 195 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 1
pypi.org: aimmd

aimmd (AI for Molecular Mechanism Discovery) autonomously steers (a large number of) molecular dynamics simulations to efficiently sampleand understand rare transition events.

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 195 Last month
Rankings
Dependent packages count: 9.7%
Average: 32.3%
Dependent repos count: 54.8%
Maintainers (1)
hejung
Last synced: 7 months ago

Dependencies

setup.py pypi
.github/workflows/publish-to-pypi.yml actions
  • actions/checkout v4 composite
  • actions/download-artifact v4 composite
  • actions/setup-python v5 composite
  • actions/upload-artifact v4 composite
  • pypa/gh-action-pypi-publish release/v1 composite
  • sigstore/gh-action-sigstore-python v3.0.0 composite
.github/workflows/tests.yml actions
  • actions/checkout v4 composite
  • codecov/codecov-action v5 composite
  • conda-incubator/setup-miniconda v3 composite
pyproject.toml pypi
  • asyncmd *
  • cython *
  • h5py >= 3
  • mdanalysis *
  • mdtraj *
  • networkx *
  • numpy >= 1.17
  • openpathsampling *