Programmable Persistent Random Walks in Active Brownian Particles Govern Emergent Dynamics

Abstract

Self-propelled particles serve as minimal models for emulating the dynamic self-organization of microorganisms, yet most synthetic systems remain limited to a single mode of motion, namely active Brownian particles (ABPs). Here, we present an experimental strategy to encode various persistent random walks in ABPs by combining light-modulated propulsion strength with magnetic control of propulsion direction. Our system enables programmable Levy walks with tunable step-length distributions, run-and-tumble dynamics, self-avoiding random walks, and Gaussian walks, with on-demand switching between motion modes within a single experiment. In addition, particles are steered along complex trajectories such as Fibonacci spirals and nested polygons. Beyond single-particle behavior, we show that propulsion modes influence clustering dynamics by comparing ABPs with chiral active particles undergoing circular motion. These results establish a versatile platform for investigating how encoded motion at the level of individual particles governs transport, search strategies, and emergent organization in active matter systems.

Published in Communications Physics (23 March 2026)

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