Metadata-Version: 2.4
Name: normal_mode_sampling_step
Version: 2026.7.15
Summary: A SEAMM plug-in for Wigner/thermal normal-mode sampling of the Hessian to generate displaced structures (e.g. for MLFF training sets)
Home-page: https://github.com/molssi-seamm/normal_mode_sampling_step
Author: Paul Saxe
Author-email: psaxe@molssi.org
License: BSD-3-Clause
Keywords: SEAMM,SEAMMplugin,flowchart
Platform: Linux
Platform: Mac OS-X
Platform: Unix
Platform: Windows
Classifier: Environment :: Plugins
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/x-rst
License-File: LICENSE
License-File: AUTHORS.rst
Requires-Dist: seamm
Requires-Dist: seamm-mdi
Requires-Dist: seamm-util
Requires-Dist: seamm-widgets
Requires-Dist: numpy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: platform
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==================================
SEAMM Normal Mode Sampling Plug-in
==================================

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A SEAMM plug-in for Wigner/thermal normal-mode sampling of the Hessian to generate displaced structures (e.g. for MLFF training sets)

* Free software: BSD-3-Clause
* Documentation: https://molssi-seamm.github.io/normal_mode_sampling_step/index.html
* Code: https://github.com/molssi-seamm/normal_mode_sampling_step

Features
--------

* Please edit this section!

Acknowledgements
----------------

This package was created with the `molssi-seamm/cookiecutter-seamm-plugin`_ tool, which
is based on the excellent Cookiecutter_.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`molssi-seamm/cookiecutter-seamm-plugin`: https://github.com/molssi-seamm/cookiecutter-seamm-plugin

Developed by the Molecular Sciences Software Institute (MolSSI_),
which receives funding from the `National Science Foundation`_ under
award CHE-2136142.

.. _MolSSI: https://molssi.org
.. _`National Science Foundation`: https://www.nsf.gov


=======
History
=======

2026.7.15 -- Initial release
    * Generates an ensemble of displaced structures by normal-mode sampling of a
      molecule's Hessian, for building machine-learned-force-field training sets
      and similar uses.
    * Amplitudes follow the quantum (Wigner) distribution by default, so stiff
      modes such as O-H stretches get the real zero-point spread that classical
      300 K sampling misses; classical-thermal and ground-state (0 K)
      distributions are also available.
    * The Hessian is obtained from the Model Chemistry defined earlier in the
      flowchart, over MDI: the analytic Hessian when the engine provides one,
      otherwise a finite-difference of the forces over the resident engine.
    * A temperature, per-mode selection, amplitude cap, and harmonic-energy
      outlier rejection are all controllable, and the random seed can be fixed
      for reproducible ensembles.
    * Off-minimum geometries are allowed with a warning, so a transition state is
      sampled along its real modes while its imaginary reaction coordinate is
      left alone.
    * Each generated structure records its predicted harmonic energy and the
      per-mode normal-coordinate displacement as properties, for downstream
      filtering and analysis.
