Analyzing the properties of clusters: Structural similarity and heat capacity

Dong, Yi; Springborg, Michael; Pang, Yong; Morillon, Francisco Morales

doi:10.1016/j.comptc.2013.06.004

Cited by 16 publications

(10 citation statements)

References 36 publications

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“…For the past several years, great efforts of researchers have addressed the studies of small cluster properties. − Such clusters play an important role in astrophysics and are constituent in the atmosphere of different planets. , Small clusters can form upon burning of hydrocarbon and metallized fuels, − in electric discharge, , and during laser ablation. ,, In addition, small clusters can be used as building blocks of new nanoscale materials with special properties. , …”

Section: Introductionmentioning

confidence: 99%

Physical and Thermodynamic Properties of Al_nC_m Clusters: Quantum-Chemical Study

2015

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Geometrical structures and physical properties, such as rotational constants and characteristic vibrational temperatures, collision diameter, enthalpy of formation, dipole moment, static isotropic polarizability, and magnetic moment of different forms of Al(n)C(m) clusters with n = 0-5, m = 0-5, have been studied with the usage of density functional theory. Different forms of clusters with the electronic energy up to 5 eV have been identified by using the original multistep heuristic algorithm based on semiempirical calculations and density functional theory. Temperature dependencies of thermodynamic properties such as enthalpy, entropy, and specific heat capacity were calculated for both the individual isomers and the Boltzmann ensembles of each class of clusters.

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Section: Introductionmentioning

confidence: 99%

Physical and Thermodynamic Properties of Al_nC_m Clusters: Quantum-Chemical Study

2015

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“…We performed MD simulations by using the density functional based tight binding (DFTB) method [44] with a Slater-Koster type DFTB-CP2K basis set [45]. Recall that DFTB method has been widely used in studies of various clusters [46][47], and the parameter adopted here has been successfully applied to various carbon-based nanostructures and agrees well with DFT calculations [48][49][50]. The MD simulations are conducted at 300 K with each structure for 3 ps under the constant-temperature constant-volume condition with the time step size as 1 fs.…”

Section: Structure and Stabilitymentioning

confidence: 99%

Silicon doping on nanotubular fullerene D5h–C90 from first principles

Bai

Xue

Tao

et al. 2015

Computational and Theoretical Chemistry

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“…Similarly, R. Hundt et al implemented an algorithm in the analysis program KPLOT 40 based on the mapping of atomic patterns constructed using three-atom frame matches. An alternative approach to the problem of structure comparison exploits the properties of the nanoclusters, 41 such as radial distribution functions, vibrational frequencies 42 or principal moments of inertia. Whichever method is used, when a structure needs to be efficiently compared with vast data for thousands or millions of congurations, the chosen approach needs to be both robust and computationally affordable.…”

Section: Methodsmentioning

confidence: 99%

What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N

Woodley

Lazauskas

Illingworth³

et al. 2018

Faraday Discuss.

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To address the question posed in the title, we have created, and now report details of, an open-access database of cluster structures with a web-assisted interface and toolkit as part of the WASP@N project. The database establishes a map of connectivities within each structure, the information about which is coded and kept as individual labels, called hashkeys, for the nanoclusters. These hashkeys are the basis for structure comparison within the database, and for establishing a map of connectivities between similar structures (topologies). The database is successfully used as a key element in a data-mining study of (MX) 12 clusters of three binary compounds (LiI, SrO and GaAs) of which the database has no prior knowledge. The structures are assessed on the energy landscapes determined by the corresponding bulk interatomic potentials. Global optimisation, using a Lamarckian genetic algorithm, is used to search for low lying minima on the same energy landscape to confirm that the data-mined structures form a representative sample of the landscapes, with only very few structures missing from the close energy neighbourhood of the respective global minima.

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Analyzing the properties of clusters: Structural similarity and heat capacity

Cited by 16 publications

References 36 publications

Physical and Thermodynamic Properties of Al_nC_m Clusters: Quantum-Chemical Study

Physical and Thermodynamic Properties of Al_nC_m Clusters: Quantum-Chemical Study

Silicon doping on nanotubular fullerene D5h–C90 from first principles

What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N

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Analyzing the properties of clusters: Structural similarity and heat capacity

Cited by 16 publications

References 36 publications

Physical and Thermodynamic Properties of AlnCm Clusters: Quantum-Chemical Study

Physical and Thermodynamic Properties of AlnCm Clusters: Quantum-Chemical Study

Silicon doping on nanotubular fullerene D5h–C90 from first principles

What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N

Contact Info

Product

Resources

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Physical and Thermodynamic Properties of Al_nC_m Clusters: Quantum-Chemical Study

Physical and Thermodynamic Properties of Al_nC_m Clusters: Quantum-Chemical Study