Low rattling: A predictive principle for self-organization in active collectives

TL;DR

This paper introduces a unifying framework based on a Boltzmann-like principle to understand and control self-organization in active collectives, validated through robotic experiments and offering insights for designing active materials.

Contribution

It presents a novel predictive principle for self-organization in active matter, linking external forcing patterns to internal dynamics for control and design.

Findings

Validated predictions with shape-changing robotic active matter

Demonstrated control of collective behavior through external forcing

Highlighted the importance of matching external patterns to internal dynamics

Abstract

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.