aimmd

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

Installation

Installing aimmd from PyPi is as easy as:

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.}

Metadata

Release files for aimmd 0.10.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for aimmd 0.10.1
File	Size	Uploaded
aimmd-0.10.1.tar.gz	268.7 kB	Jul 13, 2026	Details

Release files / aimmd-0.10.1.tar.gz

Download URL	aimmd-0.10.1.tar.gz
Size	268.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`e0780663a4dcfaa0fa14b036de0015c4f494febf87377116e545d890010964ae`
BLAKE2b-256 checksum How to use checksums	`84afcde369b2412820f718fe67222ef716db1e5b75ffd84c5f7bb84ae4cc5257`
Upload date	Jul 13, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.12`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.