SURFACE RECONSTRUCTION OF SI-(001) BY GENETIC ALGORITHM AND SIMULATEDANNEALING METHOD

The Genetic Algorithm (GA) is one of the most recently developed techn iques to find the ''Global'' minimum of an energy functional. This tec hnique combined with conjugated gradient or molecular dynamics has bee n demonstrated to be efficient for the ground-state configuration sear ch in materials research, e.g. fullerene formation, in this paper, bas ed on the generalized tight-binding molecular dynamics, we apply the G A to study the surface reconstruction of Silicon (001) for the first t ime. Up to 65 generations, the ''Global'' minimum or the ground-state configuration for the surface reconstruction of Si (001) was detected efficiently in our GA simulation. Id our tight-binding model, a perfec t symmetry-dimer structure was found to be the most energetic while so me defect asymmetry-dimer structure could coexist in the lists of cand idate structures due to the thermal defect or charge transfer which wa s described with the smearing parameter empirically. We also perform t he more traditional Simulated Annealing (SA) technique to deal with th e same problem. The results in terms of efficiency, accuracy of the gr ound-state reconstructed surface and CPU time are compared.