Modified genetic algorithms to model cluster structures in medium-size silicon clusters

Abstract

This paper presents the results obtained using a genetic algorithm (GA) to search for stable structures of medium size silicon clusters. In this work the GA uses a semiempirical energy function to find the best cluster structures, which are further optimized using density-functional theory. For small clusters our results agree well with previously reported structures, but for larger ones different structures appear. This is the case of Si 36 where we report a different structure, with significant lower energy than those previously found using limited search approaches on common structural motifs. This demonstrates the need for global optimization schemes when searching for stable structures of medium-size silicon clusters. DOI: 10.1103/PhysRevA.69.053202 PACS number(s): 36.40.2c I. INTRODUCTION The study of the structure and physical properties of atomic and molecular clusters is an extremely active area of research due to their importance, both in fundamental science and in applied technology [1]. Existing experimental methods for structural determination seldom can obtain the structure of atomic clusters directly. Therefore the calculation, using theoretical structures and comparison with experimental values of their physical and optical properties, is the most common way to obtain structural information of atomic clusters. While the prediction of the structures of clusters with a small number of atoms is well understood, the prediction of the structures and properties of medium-size (10‐ 100 atoms) clusters is much less developed in spite of their critical importance in understanding the transition from microscopic to macroscopic behavior of nanomaterials and their possible technological applications.