normal-mode-sampling-step 2026.7.15

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

SEAMM Normal Mode Sampling Plug-in

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.

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

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.