Global optimization-based dimer method for finding saddle points

TL;DR

This paper introduces a novel global optimization-based dimer method (GOD) that combines ant colony optimization with an optimization-based shrinking dimer to efficiently locate multiple saddle points on potential energy surfaces, outperforming existing methods.

Contribution

The paper presents a new framework coupling ACO with OSD for saddle point search, improving efficiency and enabling the use of various global optimization techniques.

Findings

GOD significantly outperforms the OSD method in computational efficiency.

Demonstrated effectiveness on a benchmark seven-atom island problem.

Applicable to complex potential energy surface saddle point searches.

Abstract

Searching saddle points on the potential energy surface is a challenging problem in the rare event. When there exist multiple saddle points, sampling different initial guesses are needed in most dimer-type methods in order to find distinct saddle points. In this paper, we present a novel global optimization-based dimer method (GOD) to efficiently search saddle points by coupling ant colony optimization (ACO) algorithm with optimization-based shrinking dimer (OSD) method. In particular, we apply OSD method as a local search algorithm for saddle points and construct a pheromone function in ACO to update the global population. By applying a two-dimensional example and a benchmark problem of seven-atom island on the (111) surface of an FCC crystal, we demonstrate that GOD shows a significant improvement in computational efficiency compared with OSD method. Our algorithm offers a new framework to open up possibilities of adopting other global optimization methods to search saddle points.