Optimizing Self-Assembly in Lattice Systems through Simulated Annealing: A Study of Cooling Strategies and Interaction Energies

The self-assembly is modeled by lattices containing three units of cell agents as molecules. This paper studies how various cooling strategies, including interaction parameters, act on the self- assembly of a lattice system containing three-unit cell agents of varying configurations. The self- organization is governed by attractive or repulsive forces between constituents and is a common driver of many natural processes, from the making of biological structures to that of nano-materials. This paper aims to find the energy minima using three cooling strategies: proportional, logarithmic, and exponential. The effect of interaction parameters on the system’s behavior using interactions between different agent types, positive, negative, and neutral, is studied. Simulated annealing is used in simulations to find the energy reduction and clustering behavior. The results are analyzed utilizing heat maps, energy change calculations, and the overall system organization. The cluster movements ensure that local minima are avoided. The results obtained give insight into the relationship between cooling strategies and interaction parameters on the emergent properties of the lattice system.