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

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.

References in corpus (12)

• The Mechanics and Statistics of Active Matter
• Motility-Induced Phase Separation
• Macroscopic fluctuation theory
• Statistical physics of inference: Thresholds and algorithms
• Stochastic thermodynamics for active matter
• Tuning the random walk of active colloids
• Maximum Caliber: a general variational principle for dynamical systems
• Optimal estimates of the diffusion coefficient of a single Brownian trajectory
• Lattice Dynamics and Melting of a Nonequilibrium Pattern
• Critical Phenomena in Active Matter
• Ramifications of disorder on active particles in one dimension
• Least Rattling Feedback from Strong Time-scale Separation

Cited by in corpus (15)

• Statistical mechanics of a chiral active fluid
• Order and Information in the Patterns of Spinning Magnetic Micro-disks at the Air-water Interface
• Chiral Active Particles are Sensitive Reporter to Environmental Geometry
• Maximum diffusion reinforcement learning
• Hydrodynamic effects on the liquid-hexatic transition of active colloids
• Emergent Microrobotic Oscillators via Asymmetry-Induced Order
• Cell decision-making through the lens of Bayesian learning
• Drive-specific adaptation in disordered mechanical networks of bistable springs
• Collective synchronization of undulatory movement through contact
• Dissipative self-assembly of patchy particles under nonequilibrium drive: a computational study
• Field-mediated locomotor dynamics on highly deformable surfaces
• Effects of particle angularity on granular self-organization
• A Deep Reinforcement Learning Environment for Particle Robot Navigation and Object Manipulation
• Lateral contact yields longitudinal cohesion in active undulatory systems
• A Data-Driven Statistical Description for the Hydrodynamics of Active Matter