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Updated: Jul 17, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Global minimum structure searches via particle swarm optimization.

Seth T Call¹, Dmitry Yu Zubarev, Alexander I Boldyrev

¹Department of Computer Science, Utah State University, Logan, Utah 84322-0300, USA.

Journal of Computational Chemistry

|February 15, 2007

Summary

Particle swarm optimization (PSO) is newly applied for global minimum searches in atomic systems. This efficient evolutionary method successfully identified lowest-energy structures for chemical systems, outperforming other algorithms.

Related Experiment Videos

Last Updated: Jul 17, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Area of Science:

Computational chemistry
Materials science
Chemical physics

Background:

Global optimization of potential energy surfaces is crucial for determining atomic assembly structures.
Traditional methods face challenges with efficiency and reliability for complex chemical systems.

Purpose of the Study:

To introduce and validate a novel implementation of particle swarm optimization (PSO) for global minimum structure searches in chemical systems.
To enhance the original PSO algorithm for improved efficiency and reliability in computational chemistry applications.

Main Methods:

Implementation of an improved particle swarm optimization (PSO) algorithm tailored for chemical systems.
Testing the developed PSO software on benchmark problems: LJ(26) Lennard-Jones cluster, Si(2)H(5)(-), and OH(-)(H(2)O)(3).
Comparative analysis of PSO efficiency against simulated annealing and gradient embedded genetic algorithms.

Main Results:

The novel PSO implementation successfully identified the lowest-energy structures for all tested chemical systems.
The method demonstrated fast convergence and required relatively small population sizes.
PSO showed competitive or superior efficiency compared to simulated annealing and genetic algorithms for these optimization tasks.

Conclusions:

Particle swarm optimization is a powerful and efficient tool for global minimum structure searches in chemical systems.
The developed PSO software offers a reliable and fast alternative to existing optimization techniques.
This work opens new avenues for exploring complex molecular and atomic structures using evolutionary algorithms.