A Genetic Algorithm for structural optimization

David Deaven and Kai-Ming Ho
Structural optimization of atomic clusters often require extremely long time scale simulations. By using new approaches to genetic algorithms, we are able to provide much faster structural optimization of an atomic cluster. Here, the 60 atom carbon buckyball is formed from random coordinates in our simulation.
  • The genetic algorithm.
    Inspired by the Darwinian evolution process, a population of structures is maintained. New generations are produced by ``mating'' clusters and selecting the lowest energy relaxed children.
  • Carbon clusters in a tight-binding model.
    We found fullerene cages starting from random coordinates, including the 60 atom buckyball illustrated above.
  • The mating procedure.
    We use a cut and paste mating operation. The parent clusters are split in half and the new cluster is created from one half of each parent. Mutations may also be applied.

    Here we show the energy during the process of optimization. We find the lowest energy structure (as well as many others) much faster than simulated annealing. The points marked a, b, and c correspond to the structures shown above.
    Publications: