Premelting of Al nonperfect (111) surface

Tang, Fuling; Cheng, Xu; Lü, Wei; Wei, Yu

doi:10.1016/j.physb.2009.11.058

Cited by 18 publications

(8 citation statements)

References 47 publications

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“…Moreover, the range of surface energies at (0 0 1), (1 1 0) and (1 1 1) with respect to the uncertainties of single vacancy formation energy are predicted to be (0.76, 0.87), (0.98, 1.12) and (0.68, 0.78) J m −2 , respectively (see figure 13). Values of the three surface energies calculated by density functional theory [48,63] are 0.84, 0.91 and 0.75 J m −2 , respectively, while the experimental value is 0.98 J m −2 averaged over different surfaces [64]. This indicates that our predicted ranges of the surface energies agree well with the results of density functional theory and also experimental values.…”

Section: Sensitivity Of Extrapolated Results Of Potentials To the Ref...supporting

confidence: 79%

“…13). Values of the three surface energies calculated by density functional theory [48,63] are 0.84, 0.91 and 0.75 J/m 2 , respectively, while the experimental results are an averaged result of 0.98 J/m 2 over difference surfaces. This indicates that our predicted ranges of the surface energies agree well with the results of density functional theory or experiments.…”

Section: Sensitivity Of Extrapolated Results Of Potentials To the mentioning

confidence: 94%

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A new embedded-atom method approach based on thepth moment approximation

Wang¹,

Zhu²,

Xiao

et al. 2016

J. Phys.: Condens. Matter

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Large scale atomistic simulations with suitable interatomic potentials are widely employed by scientists or engineers of different areas. The quick generation of high-quality interatomic potentials is urgently needed. This largely relies on the developments of potential construction methods and algorithms in this area. Quantities of interatomic potential models have been proposed and parameterized with various methods, such as the analytic method, the force-matching approach and multi-object optimization method, in order to make the potentials more transferable. Without apparently lowering the precision for describing the target system, potentials of fewer fitting parameters (FPs) are somewhat more physically reasonable. Thus, studying methods to reduce the FP number is helpful in understanding the underlying physics of simulated systems and improving the precision of potential models. In this work, we propose an embedded-atom method (EAM) potential model consisting of a new manybody term based on the pth moment approximation to the tight binding theory and the general transformation invariance of EAM potentials, and an energy modification term represented by pairwise interactions. The pairwise interactions are evaluated by an analytic-numerical scheme without the need to know their functional forms a priori. By constructing three potentials of aluminum and comparing them with a commonly used EAM potential model, several wonderful results are obtained. First, without losing the precision of potentials, our potential of aluminum has fewer potential parameters and a smaller cutoff distance when compared with some constantly-used potentials of aluminum. This is because several physical quantities, usually serving as target quantities to match in other potentials, seem to be uniquely dependent on quantities contained in our basic reference database within the new potential model. Second, a key empirical parameter in the embedding term of the commonly used EAM model is found to be related to the effective order of moments of local density of states. This may provide a way to improve the precision of EAM potentials further through more precise approximations to tight binding theory. In addition, some critical details about construction procedures are discussed.

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Section: Sensitivity Of Extrapolated Results Of Potentials To the Ref...supporting

confidence: 79%

Section: Sensitivity Of Extrapolated Results Of Potentials To the mentioning

confidence: 94%

A new embedded-atom method approach based on thepth moment approximation

Wang¹,

Zhu²,

Xiao

et al. 2016

J. Phys.: Condens. Matter

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“…Zou et al [81] employed VG to analyze the daily and monthly sunspot series and got some new insights. Tang et al [82] constructed and analyzed complex networks from traffic time series and indicated that complex network is a practical tool for exploring dynamics in traffic time series. Banerjee et al [83] presented a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images.…”

mentioning

confidence: 99%

Complex network analysis of time series

Gao

Small

Kurths

2016

EPL

261

135

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Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.

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“…The surface melting behaviors of macro-crystals for Al, Cu and Ag have been investigated by several researchers. 21–50 For surface melting of the Al macro-crystal (110) surface, Stoltze et al 21 applied the molecular dynamics (MD) simulations to obtain the start melting temperature and the thickness of the disordered layer, which was confirmed by Gon et al , 22,24 Dosch et al 23 and Pavlovska et al 25 Then the surface melting together with the thickness of the quasi-liquid layer on Al(110) was observed by Theis and Horn. 28 The Monte Carlo (MC) simulation employed by Cox et al 31 indicated that Al(110) would melt approximately 200 K below the bulk melting temperature.…”

Section: Introductionmentioning

confidence: 86%