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

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.