Low rattling: A predictive principle for self-organization in active collectives
This work provides a unifying framework for understanding self-organization in active collectives, which could benefit researchers designing and controlling active particle mixtures and metamaterials.
The paper introduces a Boltzmann-like principle to understand and manipulate driven self-organization in active collectives. This principle was experimentally validated using shape-changing robotic active matter, demonstrating how emergent order is sensitive to the match between external forcing and internal dynamics.
Self-organization is frequently observed in active collectives, from ant rafts to molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to identify. We offer a unifying framework that models the behavior of complex systems as largely random, while capturing their configuration-dependent response to external forcing. This allows derivation of a Boltzmann-like principle for understanding and manipulating driven self-organization. We validate our predictions experimentally in shape-changing robotic active matter, and outline a methodology for controlling collective behavior. Our findings highlight how emergent order depends sensitively on the matching between external patterns of forcing and internal dynamical response properties, pointing towards future approaches for design and control of active particle mixtures and metamaterials.