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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 FROM AUTHOR]

Starting from a hypothetical but fundamental charge/discharge sequence, the topic of nonlinear optical switching in atomic clusters built from silicon and alkali metals is opened up. The outcomes presented in this work, obtained with ab initio methods of exceptional predictive capabilities, offer strong evidences that sizable hyperpolarizability contrasts between neutral and charged alkali metal doped cluster forms might be simultaneously accomplished. The observed switching procedure involves redox polyatomic clusters formed by Si atoms. These centers function as electron acceptors at the ground state and as electron donors at the excited states facilitating low energy charge transfer transitions upon electronic excitation. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

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 Si 13 and Si 16 , 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., (© 2017 Wiley Periodicals, Inc.)