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Locating transition states is crucial for investigating transition mechanismsin wide-ranging phenomena, from atomistic to macroscale systems. Existingmethods, however, can struggle in problems with a large number of degrees offreedom, on-the-fly adaptive remeshing and coarse-graining, and energylandscapes that are locally flat or discontinuous. To resolve these challenges,we introduce a new double-ended method, the Binary-Image Transition StateSearch (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 ismore robust compared to existing bracketing methods that use only two states.We demonstrate its versatility by applying BITSS to three very differentclasses of problems: Lennard-Jones clusters, shell buckling, and multiphasephase-field models.

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