Systematic Study of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>Au</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math>to<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>Au</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:math>Gold Clusters on MgO(100)<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>F</mml:mi></mml:math>Centers Using Density-Functional Theory

Vilhelmsen, Lasse B.; Hammer, Bjørk

doi:10.1103/physrevlett.108.126101

Cited by 131 publications

(82 citation statements)

References 33 publications

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“…Genetic algorithm Main-group elements and coinage metals Li, [117] Cs, [119] B, [118] Al, [138] Ga, [139] Si, [140] Sn, [130,[134][135][136] Pb, [141] P, [142] As, [142] Bi, [143] Cu, [126,144] Ag, [94,125,126] Au [94,125,126,145] Nanoalloys Na-Si, [127] Sn-Bi, [146,147] Ag-Cu, [126] Au-Cu, [126] Au-Ag [94,125] Oxide a and other systems Li-O, [148] Be-O, [149] Mg-O, [150,151] B-O, [152] Al-O, [153] Ce-O, [154] W-O, [155] Li-F, [156] Al-H, [157] B-C, [158] Li-Al-B, [159] H 2 O-H [160] Complex systems Ag-Lig, [161] Au-Surf, [131,162] Au-Pd-Surf [162] Basin hopping Homoatomic B, <...>…”

Section: Methods System Type Clustersmentioning

confidence: 99%

“…Every newly generated cluster structure was not only characterized by its energy but also by geometrical descriptors to assure the investigation of atom configurations not located before. [131] In Figure 7c, the most stable structures of Au 8 on F/MgO, encountered by the authors during their GA-DFT run, are presented. The planar A structure is the most stable using the PBE functional, whereas the M06-L functional favors the 3D C structure (Fig.…”

Section: Review Wwwq-chemorgmentioning

confidence: 99%

“…[131] First, Vilhelmsen and Hammer studied the structures of Au 8 located on F-centers of the MgO(100) surface (For convenience, we use the shortened notation Au 8 /F/MgO in the following). They used a combination of orbital-based and grid-based computation methods to speed up the GO and performed two independent GA-DFT searches, using the PBE and M06-L [205] functionals, respectively.…”

Section: Review Wwwq-chemorgmentioning

confidence: 99%

“…Using this procedure, the authors were able to identify putative GM structures of surface bound clusters 21 and predict their fluxional behavior on O 2 adsorption, thereby enabling a qualitative explanation of the observed reactivity. [131] Another study by one of the authors on the absorption behavior of Au 8 , Pd 8 , and Au 4 Pd 4 in a metal-organic framework [162] is included in Table 1 but the number of GA-DFT investigations treating ligand-protected or surface bound clusters is very small at present. Table 1.…”

Section: Review Wwwq-chemorgmentioning

confidence: 99%

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Global optimization of clusters using electronic structure methods

Heiles

Johnston

2013

Int J of Quantum Chemistry

198

191

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Over the past decade, there has been a significant growth in the development and application of methods for performing global optimization (GO) of cluster and nanoparticle structures using first-principles electronic structure methods coupled to sophisticated search algorithms. This has in part been driven by the desire to avoid the use of empirical potentials (EPs), especially in cases where no reliable potentials exist to guide the search toward reasonable regions of configuration space. This has been facilitated by improvements in the reliability of the search algorithms, increased efficiency of the electronic structure methods, and the development of faster, multiprocessor high-performance computing architectures. In this review, we give a brief overview of GO algorithms, though concentrating mainly on genetic algorithm and basin hopping techniques, first in combination with EPs. The major part of the review then deals with details of the implementation and application of these search methods to allow exploration for global minimum cluster structures directly using electronic structure methods and, in particular, density functional theory. Example applications are presented, ranging from isolated monometallic and bimetallic clusters to molecular clusters and ligated and surface supported metal clusters. Finally, some possible future developments are highlighted.

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Section: Methods System Type Clustersmentioning

confidence: 99%

Section: Review Wwwq-chemorgmentioning

confidence: 99%

Section: Review Wwwq-chemorgmentioning

confidence: 99%

Section: Review Wwwq-chemorgmentioning

confidence: 99%

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Global optimization of clusters using electronic structure methods

Heiles

Johnston

2013

Int J of Quantum Chemistry

198

191

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“…(8), using genetic algorithm (GA) tools [22] implemented in the Atomic Simulation Environment (ASE) [23,24]. The outline of the GA used here is shown in Fig.…”

Section: Genetic Algorithm Optimizationmentioning

confidence: 99%

Nanocluster structure deduced from AC-STEM images coupled to theoretical modelling

Sukuta¹,

Bossche²,

Pedersen³

et al. 2017

Nanosystems: Phys. Chem. Math.

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Determining the atomic structure of nanoclusters is a challenging task and a critical one for understanding their chemical and physical properties. Recently, the high resolution aberration corrected scanning transmission electron microscope (AC-STEM) technique has provided valuable information about such systems, but the analysis of the experimental images has typically been qualitative rather than quantitative. A method is presented for detailed analsis of AC-STEM images combined with theoretical modelling to extract atomic coordinates. An objective function formed by a linear combination of a fit to the two-dimensional AC-STEM image plus an estimate of the cluster's energy for adding information about the third dimension is used in a global optimization algorithm to extract the atomic coordinates. The method is illustrated by analyzing model images generated for the Garzón structure of the Au 55 cluster, which is a metastable structure for the embedded atom method (EAM) potential function used here to estimate the total energy. As the method does not rely on the alignment of atom rows in the AC-STEM image, the partially disordered chiral structure of the Au 55 can successfully be determined even when a significant level of noise is added to the images.

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Oxide‐Supported Gold Clusters and Nanoparticles in Catalysis: A Computational Chemistry Perspective

Tosoni

Pacchioni

2018

ChemCatChem

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This review provides an insight into the simulation of gold nanoparticles supported on oxide surfaces, with particular emphasis on the applications in heterogeneous catalysis. Some important methodological issues are firstly addressed: the polymorphism of small gold clusters, the difficulty in determining the actual charge state of adsorbed gold atoms, the relevance of long‐range dispersion and relativistic effects and the size‐effects in modelling Au nanoparticles. Then, the adsorption of Au species on oxides is addressed by comparing bulk oxides to ultrathin oxide films supported on metals. The role of defects as nucleation centres is also discussed. Finally, this review addresses the contribution from computational studies on the mechanisms involving Au‐based heterogeneous catalysts in important reactions such as the CO oxidation, the water‐gas shift reaction and the CO2 hydrogenation to methanol.

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Systematic Study ofAu6toAu12Gold Clusters on MgO(100)FCenters Using Density-Functional Theory

Cited by 131 publications

References 33 publications

Global optimization of clusters using electronic structure methods

Global optimization of clusters using electronic structure methods

Nanocluster structure deduced from AC-STEM images coupled to theoretical modelling

Oxide‐Supported Gold Clusters and Nanoparticles in Catalysis: A Computational Chemistry Perspective

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