GLOBAL GEOMETRY OPTIMIZATION OF CLUSTERS USING A GROWTH STRATEGY OPTIMIZED BY A GENETIC ALGORITHM

摘要

A new strategy for global geometry optimization of clusters is presented. Important features are a restriction of search space to favorable nearest-neighbor distance ranges, a suitable cluster growth representation with diminished correlations, and easy transferability of the results to larger clusters. The strengths and possible limitations of the method are demonstrated for Si-10 using an empirical potential. [References: 21]