Identifying clusters as low-lying mimina--efficiency of stochastic and genetic algorithms using inexpensive electronic structure levels.

Molecular candidates possessing unconventional chemical bonding paradigms (e.g., boron wheels, molecular stars, and multicenter bonding) have attracted a great deal of attention by the computational community. The viability of such systems is necessarily assessed through the identification of the lowest lying energy forms of a given chemical composition on the potential energy surface (PES). Although dozens of search algorithms have been developed, only a few are general and simple enough to become standard everyday procedures for this purpose. The simple random search and genetic algorithm (GA) are among these: but how do these approaches perform on typical isomeric searches? The performance of three specific variants for the ab initio exploration of the PES of prototype planar tetracoordinated and hypercoordinated carbon-containing systems C(2) Al(4) and CB(6) (2-) are compared. The advantages of preoptimizing with a low-cost semiempirical method (e.g., PM6) together with the most cost-efficient GA-based variant are discussed, and the trends verified by the isomer search of the larger Si(5) Li(7) (+) clusters.