An Adaptive Genetic Algorithm for determining optimal structures for atomic clusters

TL;DR

This paper introduces an adaptive genetic algorithm tailored for optimizing atomic cluster structures, demonstrating superior accuracy in identifying lowest energy configurations of Lennard-Jones clusters compared to existing methods.

Contribution

The paper presents a novel adaptive genetic algorithm specifically designed for complex many-body atomic systems, improving structure prediction accuracy.

Findings

More accurate lowest energy structures for Lennard-Jones clusters

Outperforms previous methods in structure optimization

Demonstrates robustness in complex atomic systems

Abstract

The implementation of adaptive genetic algorithms (AGA) for optimization problems has proven to be superior than many other methods due to its nature of producing more robust and high quality solutions. Considering the complexity involved in many-body simulations, a novel AGA is proposed for applications to such systems and is specifically used to determine the lowest energy structures of various sized atomic clusters. For demonstrative purposes, we apply our method to various sized Lennard-Jones clusters and show that our results are more accurate than those found in the literature employing different methods.