Metadata-Version: 2.4
Name: aimmd
Version: 0.10.1
Summary: aimmd (AI for Molecular Mechanism Discovery) autonomously steers (a large number of) molecular dynamics simulations to efficiently sampleand understand rare transition events.
Author-email: Hendrik Jung <hendrik.jung@biophys.mpg.de>
Maintainer-email: Hendrik Jung <hendrik.jung@biophys.mpg.de>
Project-URL: Documentation, https://aimmd.readthedocs.io/en/latest/
Project-URL: Repository, https://github.com/bio-phys/aimmd.git
Project-URL: Issues, https://github.com/bio-phys/aimmd/issues
Keywords: science,MD,Molecular Dynamics,molecular-dynamics,Path Sampling,Transition Path Sampling,TPS,Machine Learning,ML,Artificial Intelligence,AI,committor,commitment probability,reaction coordinate,RC,high performance computing,HPC
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.17
Requires-Dist: cython
Requires-Dist: openpathsampling>=1.6.1
Requires-Dist: mdtraj>=1.11
Requires-Dist: networkx
Requires-Dist: h5py>=3
Requires-Dist: asyncmd>=0.4.1
Requires-Dist: tqdm>=4.62.0
Provides-Extra: docs
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: myst-nb; extra == "docs"
Requires-Dist: sphinx-book-theme; extra == "docs"
Provides-Extra: tests
Requires-Dist: pytest; extra == "tests"
Requires-Dist: pytest-asyncio; extra == "tests"
Provides-Extra: tests-all
Requires-Dist: aimmd[tests]; extra == "tests-all"
Requires-Dist: coverage; extra == "tests-all"
Requires-Dist: pytest-cov; extra == "tests-all"
Provides-Extra: examples
Requires-Dist: jupyterlab; extra == "examples"
Requires-Dist: ipywidgets; extra == "examples"
Requires-Dist: tqdm; extra == "examples"
Requires-Dist: pooch; extra == "examples"
Requires-Dist: mdanalysis; extra == "examples"
Provides-Extra: dev
Requires-Dist: aimmd[docs,examples,tests-all]; extra == "dev"
Requires-Dist: pylint; extra == "dev"
Dynamic: license-file

# aimmd

[![codecov][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd-link] [![PyPI][pypi-badge]][pypi-link]

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

**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)

## Installation

Installing aimmd from [PyPi][pypi-link] is as easy as:

```bash
pip install aimmd
```

For more see the [documentation][rtd-link].

## Documentation and Code Examples

Please see the [documentation][rtd-link] 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][rtd-link] for information.

---
<sub>This README.md is printed from 100% recycled electrons.</sub>

[codecov-link]: https://app.codecov.io/gh/bio-phys/aimmd
[codecov-badge]: https://img.shields.io/codecov/c/github/bio-phys/aimmd

[rtd-link]: https://aimmd.readthedocs.io/en/latest/
[rtd-badge]: https://readthedocs.org/projects/aimmd/badge/?version=latest

[pypi-link]: https://pypi.org/project/aimmd/
[pypi-badge]: https://img.shields.io/pypi/v/aimmd
