Optimal Sampling Tool for PyNEAPPles

About

This repository contains a representative sampler tool, rep_sampler_2d.py, for the selection of the most representative molecular geometries for spectra modeling. This uses a simulated annealing algorithm to iteratively converge on an optimal subset of geometries that minimizes Kullback-Leibler divergence between itself and the full ensemble.

It has been designed for use with the uv Python package manager.

This builds on previous work by Stepan Sršeň and Petr Slavíček.[1][2]

Installation

To install and run rep_sampler_2d from PyNEAPPles, run the following commands from the top directory:

uv venv
uv pip install -e .
uv run python

Then, in the Python environment:

import pyneapples

Using rep_sampler_2d

An example of how to use this tool is shown in repsample_testcall.py.

Essentially, an instance of the class GeomReduction is created, specifying:

• The number of geometries to expect
• The number of excited states per geometry
• The number of geometries to be included in the optimal subset
• The number of cores to be used
• The number of reduction jobs to be carried out
• Whether or not the geometries should be weighted according to spectroscopic significance

The data is then read into the instance, and reduce_geoms is called. The data can be read in numerous ways, as seen in repsample_testcall.py and repsample_acetylcall.py.

For an example workflow using the AtmoSpec photoabsorption calculation tool, the README.md in acetaldehyde contains a full breakdown.

References

[1] Š. Sršeň and P. Slavíček, J. Chem. Theory Comput., 2021, 17, 6395–6404, DOI: 10.1021/acs.jctc.1c00749

[2] Stepan Sršeň GitHub: PyNEAppLES Repository, accessed 18/03/2025.