Determination of Transitional Size of Metal Clusters

Author(s):  
Y. H. Park ◽  
I. Hijazi

We investigated the transitional size of metal clusters where the electronic effect and the size effect on the ground state structure become weaker. Identification of a transitional size cluster provides the means to efficiently determine the ground state structure of large clusters using density functional theory. Beyond the critical size of clusters, geometrical effects become important and the putative global minimum obtained from an empirical method can be used to determine the true ground state structure where the size effect on structures is less significant. We identified the lowest-energy structure using a first principles method in combination with the global search algorithm. We then used the similarity function to quantify structural difference and similarity between the global minimum obtained from an empirical method and the true ground state structure. Two structures become similar beyond a certain critical size. To investigate low-lying structures of metal clusters, we used a Monte Carlo simulated annealing method which employs the Aggregate-Volume-Bias Monte Carlo (AVBMC) algorithm. Incorporated in the Monte Carlo method is an Embedded Atom Method (EAM) potential developed by the authors.

Author(s):  
Y. H. Park ◽  
I. Hijazi

The study of metal clusters has attracted much attention in recent years. Noble metal nanoparticles are of particular interest since their chemical, thermodynamic, electronic, and optical properties make them interesting candidates as building blocks of nanostructure materials. Delineation of these properties requires a complete and definitive characterization of the cluster’s geometrical structure. To find the ground state structure for a cluster, the potential-energy surface (PES) needs to be searched. In this paper, we proposed an efficient hierarchical search method to determine a ground state structure of copper clusters using an effective Monte Carlo simulated annealing method, which employs the Aggregate-Volume-Bias Monte Carlo (AVBMC) algorithm. Incorporated in the Monte Carlo method, is an efficient Embedded Atom Method (EAM) potential developed by the authors.


2013 ◽  
Vol 117 (38) ◽  
pp. 11015-11021 ◽  
Author(s):  
Maria M. Mendes-Pinto ◽  
Elodie Sansiaume ◽  
Hideki Hashimoto ◽  
Andrew A. Pascal ◽  
Andrew Gall ◽  
...  

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