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

Area of Science:

• Complex Systems
• Active Matter Physics
• Statistical Mechanics

Background:

• Self-organization is a common phenomenon in diverse active collectives, from biological systems to engineered materials.
• Existing principles for describing non-equilibrium self-organization are limited, hindering prediction and control.
• Understanding emergent order in driven systems requires a robust theoretical framework.

Purpose of the Study:

• To develop a unifying framework for modeling and understanding driven self-organization in complex systems.
• To derive a general principle, analogous to Boltzmann's, for manipulating collective behavior.
• To experimentally validate the proposed framework using robotic active matter.

Main Methods:

• Developed a theoretical model treating complex system behavior as largely random but responsive to external forcing.
• Derived a Boltzmann-like principle to govern driven self-organization.
• Conducted experiments using shape-changing robotic active matter to validate theoretical predictions.

Main Results:

• The framework successfully models configuration-dependent responses to external forcing.
• A new principle for driven self-organization was derived and experimentally validated.
• Demonstrated control over collective behavior in robotic active matter.

Conclusions:

• Emergent order in active systems is highly sensitive to the interplay between external forcing patterns and internal dynamics.
• The developed framework provides a method for understanding and manipulating self-organization.
• Findings offer pathways for designing novel active particle mixtures and metamaterials.