Global search algorithms in surface structure determination using photoelectron diffraction

Abstract

Three different algorithms to effect global searches of the variable-parameter hyperspace are compared for application to the determination of surface structure using the technique of scanned-energy mode photoelectron diffraction (PhD). Specifically, a new method not previously used in any surface science methods, the swarm-intelligence-based particle swarm optimisation (PSO) method, is presented and its results compared with implementations of fast simulated annealing (FSA) and a genetic algorithm (GA). These three techniques have been applied to experimental data from three adsorption structures that had previously been solved by standard trial-and-error methods, namely H2O on TiO2(110), SO2 on Ni(111) and CN on Cu(111). The performance of the three algorithms is compared to the results of a purely random sampling of the structural parameter hyperspace. For all three adsorbate systems, the PSO out-performs the other techniques as a fitting routine, although for two of the three systems studied the advantage relative to the GA and random sampling approaches is modest. The implementation of FSA failed to achieve acceptable fits in these tests.