Molecular geometry optimization with a genetic algorithm

We present a method for reliably determining the lowest energy structure of an atomic cluster in an arbitrary model potential. The method is based on a genetic algorithm, which operates on a population of candidate structures to produce new candidates with lower energies. Our method dramatically outperforms simulated annealing, which we demonstrate by applying the genetic algorithm to a tight-binding model potential for carbon. With this potential, the algorithm efficiently finds fullerene cluster structures up to ${\rm C}_{60}$ starting from random atomic coordinates.Comment: 4 pages REVTeX 3.0 plus 3 postscript figures; to appear in Physical Review Letters. Additional information available under "genetic algorithms" at http://www.public.iastate.edu/~deaven

Quality criteria of genetic algorithms for structure optimization

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