Aggregation of self-propelled particles with sensitivity to local order

Area of Science:

• Physics
• Complex Systems
• Statistical Mechanics

Background:

• Self-propelled particles (SPPs) are fundamental to understanding collective behavior in biological and artificial systems.
• Traditional SPP models often exhibit purely ordered or disordered phases.
• The introduction of state-switching dynamics in SPPs offers novel emergent phenomena.

Purpose of the Study:

• To investigate the emergent collective behaviors in a system of self-propelled particles with state-switching capabilities.
• To analyze the formation and characteristics of mixed phases and particle clusters.
• To determine the role of local alignment and particle density on system dynamics.

Main Methods:

• Modeling a system of self-propelled particles (SPPs) with adaptive state switching based on local order parameter.
• Utilizing a threshold mechanism to control the transition between fast aligning and slow nonaligning states.
• Simulating systems across low and high global densities to observe clustering phenomena.

Main Results:

• A mixed phase emerges, characterized by coexisting clusters of aligning and nonaligning particles.
• Nonaligner clusters appear suddenly with increasing threshold, exhibiting coalescence and fragmentation.
• System behavior differs significantly between low and high densities, with logarithmic growth of nonaligner clusters at low densities and percolation transitions at high densities.
• Speed differences between particle types are essential for segregation.

Conclusions:

• The addition of state-switching dynamics to SPPs leads to complex mixed phases and emergent clustering.
• Density plays a critical role in the nature and scale of nonaligner clustering, from logarithmic growth to percolation.
• Segregation of particle types is contingent upon differential speeds, highlighting the importance of inter-particle interactions.