Lane-formation vs. cluster-formation in two dimensional square-shoulder systems: A genetic algorithm approach

TL;DR

This paper uses genetic algorithms to predict the most stable arrangements of particles in a square shoulder system, revealing how particles self-organize into lanes or clusters depending on system parameters.

Contribution

It introduces genetic algorithms as an effective method to identify equilibrium structures in complex particle systems, advancing understanding of self-assembly behaviors.

Findings

Identifies minimum energy configurations across different parameters.

Reveals conditions favoring lane vs. cluster formation.

Provides comprehensive sequences of stable structures.

Abstract

Introducing genetic algorithms as a reliable and efficient tool to find ordered equilibrium structures, we predict minimum energy configurations of the square shoulder system for different values of corona width $\lambda$. Varying systematically the pressure for different values of $\lambda$ we obtain complete sequences of minimum energy configurations which provide a deeper understanding of the system's strategies to arrange particles in an energetically optimized fashion, leading to the competing self-assembly scenarios of cluster-formation vs. lane-formation.