Studying the varied shapes of gold clusters by an elegant optimization algorithm that hybridizes the density functional tight-binding theory and the density functional theory

Yen, T. W.; Lim, Thong Leng; Yoon, Tiem Leong; Lai, S. K.

doi:10.1016/j.cpc.2017.07.002

Cited by 23 publications

(19 citation statements)

References 42 publications

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“…Hence, provided it is used within the region of parameterization, DFTB can be an efficient method for metal cluster GO, which was recently shown for TBDFT applications to silver and gold clusters [96]. A study has also been reported which combines TBDFT and DFT directly in the GO of gold clusters [97].…”

Section: Global Optimization Methods For Metal Clusters and Nanoalloysmentioning

confidence: 99%

First principles global optimization of metal clusters and nanoalloys

Jäger

Johnston

2018

Advances in Physics: X

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The global optimization of nanoparticles, such as pure or bimetallic metal clusters, has become a very important and sophisticated research field in modern nanoscience. The possibility of using more rigorous quantum chemical first principle methods during the global optimization has been facilitated by the development of more powerful computer hardware as well as more efficient algorithms. In this review, recent advances in first principle global optimization methods are described, with the main focus on genetic algorithms coupled with density functional theory for optimizing sub-nanometre metal clusters and nanoalloys.

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Section: Global Optimization Methods For Metal Clusters and Nanoalloysmentioning

confidence: 99%

First principles global optimization of metal clusters and nanoalloys

Jäger

Johnston

2018

Advances in Physics: X

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“…A first family of GO schemes rely on genetic algorithms [152] often used to search for atomic cluster structures [63,[153][154][155][156][157]. Simulated annealing [158] as well as basinhopping schemes [159,160] have also often been used either in their standard form [161][162][163] or improved versions like the modified basin hopping [164,165] or the Tsinghua global minimum algorithms [166]. Other approaches rely on the exploration of the complex potential energy surface (PES) with either MC or MD simulations, which are combined with regular local optimization of the visited geometries as done for ammonium/water clusters [167].…”

Section: Global Exploration Of the Energy Landscape And Dynamicsmentioning

confidence: 99%

“…DFTB has been used to investigate various clusters including sodium [262], ceria [295], cadmium sulfides [233,264], boron [166], silver and gold [155,157,165,172,173,[267][268][269][270][271][272], ZnO [273], molybdenum disulfide [274], iron [154,275] or nanodiamond [276,277]. In addition to the necessary work dedicated to specific DFTB parametrization for these systems [155,156,172,173,[268][269][270]278], a number of studies have been devoted to their structural characterisation [63,153,154,157,161,165,268,278]. Figure 3 illustrates examples of investigated structures for silver cluster Ag 561 [172].…”

Section: Clusters and Nanoparticlesmentioning

confidence: 99%

“…Figure 3 illustrates examples of investigated structures for silver cluster Ag 561 [172]. An interesting question is the evolution with size of the competition between ordered and disordered structures [157,165,173,272]. For instance, global exploration performed at the DFTB level followed by local optimization at the DFT level, suggested that Au 55 presents cavities [173] (recently confirmed by two other DFT studies [63,279]), and showed that the amorphous forms of Au 147 are expected to be more stable than the regular icosahedral ones, or at least very competitive with this latter at low temperature [272] (see Figure 4).…”

Section: Clusters and Nanoparticlesmentioning

confidence: 99%

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Density-functional tight-binding: basic concepts and applications to molecules and clusters

Spiegelman

Tarrat

Cuny

et al. 2020

Advances in Physics: X

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The scope of this article is to present an overview of the Density Functional based Tight Binding (DFTB) method and its applications. The paper introduces the basics of DFTB and its standard formulation up to second order. It also addresses methodological developments such as third order expansion, inclusion of non-covalent interactions, schemes to solve the self-interaction error, implementation of long-range short-range separation, treatment of excited states via the time-dependent DFTB scheme, inclusion of DFTB in hybrid high-level/low level schemes (DFT/DFTB or DFTB/MM), fragment decomposition of large systems, large scale potential energy landscape exploration with molecular dynamics in ground or excited states, nonadiabatic dynamics. A number of applications are reviewed, focusing on -(i)-the variety of systems that have been studied such as small molecules, large molecules and biomolecules, bare or functionalized clusters, supported or embedded systems, and -(ii)-properties and processes, such as vibrational spectroscopy, collisions, fragmentation, thermodynamics or non-adiabatic dynamics. Finally outlines and perspectives are given. ARTICLE HISTORY

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“…Then the core radius is doubled to get the second sequence of clusters. The process continues doubling the core radius and generates more sequences of clusters until the number of clusters and other validation criteria stabilize, which is called a turn point (Yen et al, 2017).…”

Section: Parameter Reductionmentioning

confidence: 99%

IDCUP Algorithm to Classifying Arbitrary Shapes and Densities for Center-based Clustering Performance Analysis

Kazmi

2020

IJIKM

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Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

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Studying the varied shapes of gold clusters by an elegant optimization algorithm that hybridizes the density functional tight-binding theory and the density functional theory

Cited by 23 publications

References 42 publications

First principles global optimization of metal clusters and nanoalloys

First principles global optimization of metal clusters and nanoalloys

Density-functional tight-binding: basic concepts and applications to molecules and clusters

IDCUP Algorithm to Classifying Arbitrary Shapes and Densities for Center-based Clustering Performance Analysis

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