A robust and memory-efficient transition state search method for complex energy landscapes

TL;DR

The paper introduces BITSS, a new memory-efficient and robust transition state search method that effectively handles complex energy landscapes across various systems.

Contribution

A novel double-ended transition state search method, BITSS, that improves robustness and efficiency in high-dimensional and complex energy landscapes.

Findings

BITSS converges faster than existing methods.

It is more robust in flat or discontinuous landscapes.

Successfully applied to diverse physical systems.

Abstract

Locating transition states is crucial for investigating transition mechanisms in wide-ranging phenomena, from atomistic to macroscale systems. Existing methods, however, can struggle in problems with a large number of degrees of freedom, on-the-fly adaptive remeshing and coarse-graining, and energy landscapes that are locally flat or discontinuous. To resolve these challenges, we introduce a new double-ended method, the Binary-Image Transition State Search (BITSS). It uses just two states that converge to the transition state, resulting in a fast, flexible, and memory-efficient method. We also show it is more robust compared to existing bracketing methods that use only two states. We demonstrate its versatility by applying BITSS to three very different classes of problems: Lennard-Jones clusters, shell buckling, and multiphase phase-field models.