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Global geometry optimization of metal clusters is an important problem in nanophysics. The starting geometries of the clusters generated with empirical or other model potentials are generally optimized further by density functional theory (DFT)-based energy minimization. For this purpose, several algorithms such as simulated annealing, genetic algorithms, basin hopping, etc. are used. Our building-up procedure generates putative lower-energy structures of metal (M) clusters, Mn+1, Mn+2, etc., by anchoring one or more metal atoms in the vicinity of the minima of the molecular electrostatic potential (MESP) of Mn. Here, we report an application of this method to Agnclusters, for 5 ≤ n≤ 20, followed up by DFT-based geometry optimization, generating several lower-energy structures than those reported in the literature. New low-energy isomers are obtained by applying the same procedure to the test case of mixed-metal clusters, NinAgm, for n+ m= 4 and 5. In conclusion, our MESP-based building-up procedure offers a new general methodology for generating lower-energy geometries of metal clusters.

We study the interplay between surface and bulk properties of macroscopic materials. It is demonstrated that the so-called polar surfaces may be stabilized through a charge redistribution between the complete set of surfaces that depends upon the overall shape of the sample and the nature of the material. This charge redistribution, in turn, is governed by certain constraints that we call generalized Tasker conditions. The same surface, but for samples of different shapes, may have different surface charges. Besides its stabilizing effect, the charge redistribution is also shown to particularly affect the dipole moment per repeat unit, a bulk property. For the latter, it is established that essentially any physically meaningful value is possible (depending upon the shape and material), in contrast to the often made assumption that different samples of the same material will have values that differ by, at most, a lattice vector. Finally, some recent experimental and theoretical results for polar surfaces are discussed in terms of the analysis presented here.

The putative global minimum structures of bimetallic CumAgnnanoalloys for all (m,n) with N= m+ nfrom 2 to 60 atoms have been determined. The embedded-atom method was used for the description of the interatomic interactions in combination with the basin-hopping algorithm for the global structural optimization. The obtained global-minimum structures are mostly based on icosahedra, polyicosahedra as well as 5-fold pancakes. But also truncated octahedral are found for some clusters with 38 atoms. The bond-order parameter reveals the formation of CucoreAgshellclusters. The analysis of energetic properties, through stability functions, the excess and the mixing energies, gives that the clusters with more Ag atoms are energetically more favorable. Moreover, from our analysis we identify the most stable stoichiometries as a function of N. Finally, the results of the present study are compared with similar results on NimAgnclusters.

The structure of the global total-energy minimum for all bimetallic NimAgnnanoalloys with m+ n= N= 2−60 atoms has been identified theoretically by combining the embedded-atom model for the total-energy evaluation with the basin-hopping algorithm for global structure optimization. All global minima structures are found to be related to icosahedra and polyicosahedra, except for some Ni- or Ag-rich clusters for N= 38. Through a careful analysis of the total energy as a function of (m,n), various particularly stable structures can be identified. The results show in most cases that Ag-rich structures are more favored and stable compared with the Ni-rich structures. By analyzing the bond-order parameter and the radial distances, we can demonstrate the existence of core−shell structures with a partial segregation of Ag to the surface of the Ni−Ag clusters.

We present an improved inverse-design approach for automatically identifying molecular (or other) systems with optimal values for prechosen properties. The new approach uses SMILES (simplified molecular input line entry system) to describe molecular structures efficiently, a genetic algorithm to optimize the molecules automatically, and the DFTB+ (self-consistent charge density functional tight-binding) method to calculate electronic properties. Thereby, almost every class of materials─even macromolecules or monomers─can be studied easily. Without crossover operators but with only mutation operators, the genetic algorithm is more adaptive to SMILES while keeping its efficiency. DFTB+ is more accurate than the DFTB method used in our previous inverse-design approach for the study of excited states and charge transfer processes. The improved approach is applied to optimize benzene, pyridine, pyridazine, pyrimidine, and pyrazine derivatives for seven electronic properties, which all are highly relevant and important for the performance of molecules in solar cells. We found that for some electronic properties, the precise composition and structure of the backbone have remarkable impacts on the value of the electronic properties and/or on the set of functional groups that leads to the best performance. On the contrary, for other properties, these effects are less pronounced. The reasonable optimal functional groups and/or substitution patterns are reported.