Target search of active agents crossing high energy barriers

TL;DR

This paper introduces a novel algorithm for target search in rugged energy landscapes with active agents, overcoming limitations of traditional methods for irreversible dynamics, and demonstrates significant differences in search behavior due to activity.

Contribution

The authors develop a generalized transition-path sampling algorithm for active Brownian dynamics, enabling efficient target search analysis in non-equilibrium systems.

Findings

Active particles reach targets more frequently than passive ones.

Activity drastically alters the structure and kinetics of transition paths.

Active search patterns are longer and counterintuitive.

Abstract

Target search by active agents in rugged energy landscapes has remained a challenge because standard enhanced sampling methods do not apply to irreversible dynamics. We overcome this non-equilibrium rare-event problem by developing an algorithm generalizing transition-path sampling to active Brownian dynamics. This method is exemplified and benchmarked for a paradigmatic two-dimensional potential with a high barrier. We find that even in such a simple landscape the structure and kinetics of the ensemble of transition paths change drastically in the presence of activity. Indeed, active Brownian particles reach the target more frequently than passive Brownian particles, following longer and counterintuitive search patterns.