A Fukui function‐guided genetic algorithm. Assessment on structural prediction of Sin (n = 12–20) clusters. Issue 19 (24th April 2017)

• Record Type: Journal Article
• Authors: Yañez, Osvaldo; Vásquez‐Espinal, Alejandro; Inostroza, Diego; Ruiz, Lina; Pino‐Rios, Ricardo; Tiznado, William
• Is Part Of: Journal of computational chemistry. Volume 38:Issue 19(2017)
• Page Start: 1668
• Page End: 1677
• Publication Date: 2017-04-24
• Subjects: Fukui function -- clusters -- genetic algorithm -- potential energy surface exploration; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.24810
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract : Theoretical studies are essential for the structural characterization of clusters, when it comes to rationalize their unique size‐dependent properties and composition. However, the rapid growth of local minima on the potential energy surface (PES), with respect to cluster size, makes the candidate identification a challenging undertaking. In this article, we introduce a hybrid strategy to explore the PES of clusters. This proposal involves the use of a biased initial population of a genetic algorithm procedure. Each individual in this population is built by assembling small fragments, according to the best matching of the Fukui function. The performance of a genetic algorithm procedure. The performance of the method is assessed on the PES exploration of medium‐sized Si n clusters ( n = 12–20). The most relevant results are: (a) the method converges at almost half of the time used by the canonical version of the GA and, (b) in all the studied cases, with the exception of Si13 and Si16, the method allowed to identify the global minimum (GM) and other important low‐lying structures. Additionally, the apparent deficiency of the proposal to identify the GM was corrected when a Si atom, or other low‐lying isomers, were considered to build the clusters. © 2017 Wiley Periodicals, Inc. Abstract : In this article, a hybrid strategy to explore the potential energy surface of clusters is introduced. This proposal involves the use of a biased initial population of a genetic algorithm procedure. Each individual in this population is built by assembling small fragments, according to the best matching of the Fukui function. After the assessment on the PES exploration of medium‐sized Si n clusters ( n = 12–20), the method shows to be efficient on both identifying global minimum and local minimum structures, and decreasing computational time.