Structure and Mobility of Metal Clusters in MOFs: Au, Pd, and AuPd Clusters in MOF-74

Vilhelmsen, Lasse B.; Walton, Krista S.; Sholl, David S.

doi:10.1021/ja305004a

Cited by 143 publications

(103 citation statements)

References 68 publications

(120 reference statements)

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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%

“…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%

See 1 more Smart Citation

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%

Global optimization of clusters using electronic structure methods

Heiles

Johnston

2013

Int J of Quantum Chemistry

198

191

View full text Add to dashboard Cite

show abstract

“…The required number of parent structures are drawn from the population with a bias towards the more stable and less frequently selected structures (as in Ref. [26]). In the case of crossover, the offspring is produced through standard cut-and-splice pairing of two parent structures [27].…”

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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“…Over the past several decades, a large body of atomistic simulations on clusters (especially metallic clusters) has been performed using Finnis-Sinclair potentials [13,14], embedded-atom method (EAM) potentials [15,16], tight-binding method potentials [15,17] and first-principles calculations based on density functional theory (DFT) [18,19] to explore their structure and energetic stability, growth, interaction with surfaces, phase transitions, strain and surface energies, melting and freezing behaviors.…”

Section: Introductionmentioning

confidence: 99%