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aimmd

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

aiartificialcommitmentcommittorcomputingcoordinatedynamicshighhpcintelligencelearningmachinemdmlmolecularmolecular-dynamics
All keywords (25)
pathperformanceprobabilityrcreactionsamplingsciencetpstransition
Versions collected1
Direct dependencies21From release 0.10.1
Last observed21 Oct, 11:20 UTC

README

Version 0.10.1 documentation

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.

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.

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VersionPublishedDirect dependenciesOpen version
0.10.113 Jul 202621

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bio-phys/aimmd

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Observed since 21 Oct, 11:20 UTC

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package:ad590f39a7b777a97ff3985fd50e82059407f1fd0e837700bb7be3cbb3aa8fc7