Parallel J-W Monte Carlo simulations of thermal phase changes in finite-size systems

Radev, Rossen; Proykova, Ana

doi:10.1016/s0010-4655(02)00255-2

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…26 Most often, however, a value in the range of 0.08-0.15 is used for various nanocluster materials. 18,21,41,42 In this work, we employed a critical value of 0.08. 42 Note that the melting point is, in fact, a macroscopic concept, defined as the temperature at which a solid becomes liquid.…”

Section: Definition Of Melting Pointmentioning

confidence: 99%

“…In the literature, different criteria have been used to determine the melting point, corresponding to average Lindemann indices ranging from 0.03 for CNTs to 0.24 . Most often, however, a value in the range of 0.08−0.15 is used for various nanocluster materials. ,,, In this work, we employed a critical value of 0.08 . Note that the melting point is, in fact, a macroscopic concept, defined as the temperature at which a solid becomes liquid.…”

Section: Definition Of Melting Pointmentioning

confidence: 99%

“…18,21,41,42 In this work, we employed a critical value of 0.08. 42 Note that the melting point is, in fact, a macroscopic concept, defined as the temperature at which a solid becomes liquid. Because of the finite size, however, melting occurs over a range of temperatures, in which the solid and liquid phases can coexist with different fractions.…”

Section: Definition Of Melting Pointmentioning

confidence: 99%

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Numerical Study of the Size-Dependent Melting Mechanisms of Nickel Nanoclusters

Neyts

Bogaerts

2009

J. Phys. Chem. C

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Molecular dynamics simulations were used to investigate the size-dependent melting mechanism of nickel nanoclusters of various sizes. The melting process was monitored by the caloric curve, the overall cluster Lindemann index, and the atomic Lindemann index. Size-dependent melting temperatures were determined, and the correct linear dependence on inverse diameter was recovered. We found that the melting mechanism gradually changes from dynamic coexistence melting to surface melting with increasing cluster size. These findings are of importance in better understanding carbon nanotube growth by catalytic chemical vapor deposition as the phase state of the catalyst nanoparticle codetermines the growth mechanism.

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Section: Definition Of Melting Pointmentioning

confidence: 99%

Section: Definition Of Melting Pointmentioning

confidence: 99%

Section: Definition Of Melting Pointmentioning

confidence: 99%

See 1 more Smart Citation

Numerical Study of the Size-Dependent Melting Mechanisms of Nickel Nanoclusters

Neyts

Bogaerts

2009

J. Phys. Chem. C

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“…The potential, equation (20), has been used in molecular dynamics and Monte Carlo [107] simulations of clusters, initially arranged as spherical as possible, figure 1 in [100], at a constant energy [11,25,99] and at a constant temperature [24,108]. The aims of simulations have been to:…”

Section: Temperature-driven Phase Transitionsmentioning

confidence: 99%

Insights into phase transitions from phase changes of clusters

Proykova¹,

Berry²

2006

J. Phys. B: At. Mol. Opt. Phys.

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The phases and phase transitions of bulk matter differ in several important ways from the phases or phase-like forms of small systems, notably atomic and molecular clusters. However, understanding those differences gives insights into the nature of bulk transitions, as well as into understanding the behaviour of the small systems. Small systems exhibit dynamic phase equilibria, large fluctuations and size-dependent behaviour in ways one cannot see with macroscopic systems. The Gibbs phase rule loses its applicability, the distinction between first-order and second-order transitions reveals a size dependence, and one can see phases in equilibrium (as minority populations) that are never the most stable thermodynamically. Introduction: phases of clusters and of macroscopic systemsThis review addresses the relations between phases and phase changes exhibited by bulk, macroscopic systems and their counterparts in small systems, atomic and molecular clusters and nanoscale particles. The differences between the phase behaviour of bulk matter and of clusters can, for the most part, be attributed to two factors. The more important is the enormous free energy differences between phases of bulk matter, which make only the most favoured phase observable, compared with the small free energy differences for clusters, which make less favoured phases nearly as observable as more favoured or most favoured phases [1]. The other is the relative contribution of the surface in a macroscopic and a small system. In a d-dimensional system containing N atoms, approximately N (d−1)/d of atoms will be on the surface; for large N the relative fraction N −1/d is very small and the surface effects on the bulk properties of the system are commonly neglected. In contrast, a high proportion of atoms or molecules are in surface layers of clusters which makes the surface as important for many cluster properties as the interior part is. In the spherical approximation used above to estimate the surface contribution one finds that about one half of the particles are on the surface of a cluster containing 500 particles. Because the ratio of surface area to bulk is large for small

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“…The melting point was determined as the temperature at which the Lindemann index changed discontinuously. In previous studies, a threshold value in the range of 0.08-0.15 was used as the melting point criteria [49][50][51][52][53][54]. However, in the case of small nanoparticles, the solid and liquid phases coexist over a wide temperature range and the criteria for the melting point has not been well established.…”

Section: Thermodynamic Properties Of Fe Bulk and Nanoparticlesmentioning

confidence: 99%

Catalyzed Single-Walled Carbon Nanotube Growth by Machine Learning Molecular Dynamics Simulation

Kohata¹,

Yoshikawa²,

Hisama³

et al. 2023

Preprint

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Classical molecular dynamics (MD) simulations have been performed to elucidate the mechanism of singlewalled carbon nanotube (SWCNT) growth. However, discussing the chirality has been challenging due to topological defects in simulated SWCNTs. Recently, the application of neural network to interatomic potentials has been actively studied and such interatomic potentials are called neural network potentials (NNPs). NNPs have a better ability for approximate functions and can predict complex systems' energy more accurately than conventional interatomic potentials. In this study, we developed an NNP to describe SWCNT growth more accurately.We successfully demonstrated defect-free and chirality-definable growth of SWCNTs on Fe nanoparticles by MD simulation. Furthermore, it was revealed that edge vacancies of SWCNTs caused defect formation and adatom diffusion contributed to vacancy healing, preventing defect formation.

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Parallel J-W Monte Carlo simulations of thermal phase changes in finite-size systems

Cited by 4 publications

References 9 publications

Numerical Study of the Size-Dependent Melting Mechanisms of Nickel Nanoclusters

Numerical Study of the Size-Dependent Melting Mechanisms of Nickel Nanoclusters

Insights into phase transitions from phase changes of clusters

Catalyzed Single-Walled Carbon Nanotube Growth by Machine Learning Molecular Dynamics Simulation

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