title: A strategy to find minimal energy nanocluster structures.
abstract: An unbiased strategy to search for the global and local minimal energy structures of free standing nanoclusters is presented. Our objectives are twofold: to find a diverse set of low lying local minima, as well as the global minimum. To do so, we use massively the fast inertial relaxation engine algorithm as an efficient local minimizer. This procedure turns out to be quite efficient to reach the global minimum, and also most of the local minima. We test the method with the Lennard-Jones (LJ) potential, for which an abundant literature does exist, and obtain novel results, which include a new local minimum for LJ13 , 10 new local minima for LJ14 , and thousands of new local minima for 15≤N≤65. Insights on how to choose the initial configurations, analyzing the effectiveness of the method in reaching low-energy structures, including the global minimum, are developed as a function of the number of atoms of the cluster. Also, a novel characterization of the potential energy surface, analyzing properties of the local minima basins, is provided. The procedure constitutes a promising tool to generate a diverse set of cluster conformations, both two- and three-dimensional, that can be used as an input for refinement by means of ab initio methods.
authors.affiliation: Departamento de Física, Facultad de Ciencias, Universidad de Chile, Casilla 653, Santiago, Chile 7800024, and Centro para el Desarrollo de la Nanociencia y la Nanotecnología (CEDENNA), Avda., Ecuador 3493, Santiago, Chile, 9170124.
authors.forename: José
authors.initials: J
authors.lastname: Rogan
authors.forename: Alejandro
authors.initials: A
authors.lastname: Varas
authors.forename: Juan Alejandro
authors.initials: JA
authors.lastname: Valdivia
authors.forename: Miguel
authors.initials: M
authors.lastname: Kiwi
mesh_terms: D000465:Algorithms; D049329:Nanostructures / Q000737:chemistry*; D013816:Thermodynamics
keywords: Lennard-Jones pair potential; energy landscape; global minimum; local minima; optimization