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Updated: May 8, 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

Single string based global optimizer for geometry optimization in strongly coupled finite clusters: An adaptive

Kanchan Sarkar¹, S P Bhattacharyya

¹Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700032, India.

The Journal of Chemical Physics

|August 24, 2013

Summary

We developed a new adaptive algorithm to find the lowest energy structures for charged particle clusters. This method efficiently explores complex energy landscapes, outperforming existing global optimizers.

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Area of Science:

Computational Physics
Materials Science
Chemical Physics

Background:

Optimizing cluster geometries is crucial for understanding materials properties.
Complex potential energy landscapes pose challenges for traditional optimization methods.
Existing global optimizers may lack efficiency in exploring vast search spaces.

Purpose of the Study:

To introduce a novel adaptive heuristic for global minimum energy configuration searches.
To implement and test the Completely Adaptive Random Mutation Hill Climbing (CARMHC) method.
To analyze the performance of CARMHC on various complex potential energy landscapes.

Main Methods:

Developed a simple adaptive heuristic using random mutations and energy-based acceptance criteria.
Implemented CARMHC with adaptive mutation probability and intensity.
Applied CARMHC to parabolically confined 3D classical Coulomb clusters and MgO/Lennard-Jones clusters.

Main Results:

CARMHC successfully identified global minimum energy configurations for diverse cluster systems.
Analysis revealed relationships between confinement strength, particle number, and cluster properties.
Identified specific 'magic numbers' indicating stable cluster configurations.
Demonstrated favorable convergence behavior compared to other global optimization techniques.

Conclusions:

The CARMHC method is a robust and efficient tool for global geometry optimization of atomic and ionic clusters.
The algorithm's adaptive nature allows for effective navigation of complex energy landscapes.
CARMHC shows significant promise for applications in plasma physics and materials discovery.