New candidates for the global minimum of medium-sized silicon clusters: A hybrid DFTB/DFT genetic algorithm applied to Si<i>n</i>, <i>n</i> = 8-80

Heydariyan, Shima; Nouri, Mohammad Reza; Alaei, Mojtaba; Allahyari, Zahed; Niehaus, Thomas A.

doi:10.1063/1.5037159

Cited by 15 publications

(19 citation statements)

References 96 publications

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“…As mentioned in point (3), our cluster structures do not necessarily correspond to global minima. These could be in principle obtained by global optimization, e.g., by simulated annealing techniques , or by genetic algorithms. , We followed a different strategy based on the concept of surface energy minimization. The shape of microcrystals with minimal free surface energy is obtained applying the Wulff theorem .…”

Section: Methodsmentioning

confidence: 99%

Spectroscopic Properties of Chalcopyrite Nanoparticles

Thinius

Bredow

2019

J. Phys. Chem. C

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An approach is presented to construct stoichiometric and saturated clusters as representative models for nanoparticles based on quantum-chemical surface energy calculations. The procedure consists of three steps. In the first step, the shape of the cluster is determined by applying the Wulff theorem based on calculated surface energies of the solid compound. Stoichiometry is recovered by adding selected atoms on the cluster surface. If the particles in solution are to be modeled, solvent molecules may be added. A global optimization is then performed to allow for full reconstruction of the cluster structure. As an example, we studied the spectroscopic properties of chalcopyrite (CuFeS2) nanoparticles and compared our calculated results to experimental O 1s X-ray photoelectron spectroscopy, IR, and optical spectra.

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Section: Methodsmentioning

confidence: 99%

Spectroscopic Properties of Chalcopyrite Nanoparticles

Thinius

Bredow

2019

J. Phys. Chem. C

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“…GA can be utilized for optimizing different kinds of systems 60 , and the procedure is rather standardized for structural optimization of atomic systems [61][62][63][64][65] . The genes in this case consist of the coordinates of the atoms of interest, and elemental composition for multi-elemental systems.…”

Section: A Neural Network Potentialmentioning

confidence: 99%

Global Optimization of Copper Clusters at the ZnO(10-10) Surface Using a DFT-based Neural Network Potential and Genetic Algorithms

Paleico¹,

Behler²

2020

Preprint

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The determination of the most stable structures of metal clusters supported at solid surfaces by computer simulations represents a formidable challenge due to the complexity of the potential-energy surface. Here we combine a highdimensional neural network potential, which allows to predict the energies and forces of a large number of structures with first-principles accuracy, with a global optimization scheme employing genetic algorithms. This very efficient setup is used to identify the global minima and low-energy local minima for a series of copper clusters containing between four and ten atoms adsorbed at the ZnO(10 10) surface. A series of structures with common structural features resembling the Cu(111) and Cu(110) surfaces at the metal-oxide interface has been identified, and the geometries of the emerging clusters are characterized in detail. We demonstrate that the frequently employed approximation of a frozen substrate surface in global optimization can result in missing the most relevant structures.

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“…GAs have found many successful applications, e.g. in crystal structure prediction 21 , optimization of free and supported atomic clusters [22][23][24][25][26] , and proteins 27 .…”

Section: Introductionmentioning

confidence: 99%

A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo

Paleico

Behler

2020

The Journal of Chemical Physics

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Global optimization is an active area of research in atomistic simulations, and many algorithms have been proposed to date. A prominent example is basin hopping Monte Carlo, which performs a modified Metropolis Monte Carlo search to explore the potential energy surface of the system of interest. These simulations can be very demanding due to the high-dimensional configurational search space. The effective search space can be reduced by utilizing grids for the atomic positions, but at the cost of possibly biasing the results if fixed grids are employed. In this paper, we present a flexible grid algorithm for global optimization that allows to exploit the efficiency of grids without biasing the simulation outcome. The method is general and applicable to very heterogeneous systems, such as interfaces between two materials of different crystal structure or large clusters supported at surfaces. As a benchmark case, we demonstrate its performance for the well-known global optimization problem of Lennard-Jones clusters containing up to 100 particles. In spite of the simplicity of this model potential, Lennard-Jones clusters represent a challenging test case, since the global minima for some "magic" numbers of particles exhibit geometries that are very different from those of clusters with only a slightly different size.

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New candidates for the global minimum of medium-sized silicon clusters: A hybrid DFTB/DFT genetic algorithm applied to Sin, n = 8-80

Cited by 15 publications

References 96 publications

Spectroscopic Properties of Chalcopyrite Nanoparticles

Spectroscopic Properties of Chalcopyrite Nanoparticles

Global Optimization of Copper Clusters at the ZnO(10-10) Surface Using a DFT-based Neural Network Potential and Genetic Algorithms

A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo

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