The Experts below are selected from a list of 28305 Experts worldwide ranked by ideXlab platform
Yiping Tang - One of the best experts on this subject based on the ideXlab platform.
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a study of associating lennard jones chains by a new reference Radial Distribution Function
Fluid Phase Equilibria, 2000Co-Authors: Yiping TangAbstract:Abstract A new Radial Distribution Function (RDF) for the Lennard–Jones (LJ) fluid is derived around the LJ potential size (σ). The theoretically based RDF is completely analytical and real. Comparisons with computer simulation data at various conditions indicate that the RDF is very accurate at r
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direct calculation of Radial Distribution Function for hard sphere chains
Journal of Chemical Physics, 1996Co-Authors: Yiping Tang, Benjamin C Y LuAbstract:The Laplace transform of the average Radial Distribution Function of hard‐sphere chains is obtained following the approximation suggested by Chiew [Mol. Phys. 73, 359 (1991)]. The transform expression is of a simple analytical form. The inverse Laplace transform is made analytically. The resulting expression is capable of calculating directly the Radial Distribution Function of hard‐sphere chains for any value of r.
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Direct calculation of Radial Distribution Function for hard‐sphere chains
The Journal of Chemical Physics, 1996Co-Authors: Yiping TangAbstract:The Laplace transform of the average Radial Distribution Function of hard‐sphere chains is obtained following the approximation suggested by Chiew [Mol. Phys. 73, 359 (1991)]. The transform expression is of a simple analytical form. The inverse Laplace transform is made analytically. The resulting expression is capable of calculating directly the Radial Distribution Function of hard‐sphere chains for any value of r.
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improved expressions for the Radial Distribution Function of hard spheres
Journal of Chemical Physics, 1995Co-Authors: Yiping TangAbstract:The solution of the first‐order Ornstein–Zernike equation is applied to improve the Percus–Yevick Radial Distribution Function (RDF) of hard spheres, where the direct correlation Function is postulated to hold the Yukawa form outside the hard core. Thermodynamic consistency is imposed to determine the parameters in the postulation. Very simple analytical expressions for the Laplace transform of the RDF are obtained for hard spheres and hard sphere mixtures. The resulting RDFs are compared satisfactorily with computer simulation data.
Andrés Santos - One of the best experts on this subject based on the ideXlab platform.
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Radial Distribution Function for hard spheres in fractal dimensions a heuristic approximation
Physical Review E, 2016Co-Authors: Andrés Santos, Mariano Lopez De HaroAbstract:: Analytic approximations for the Radial Distribution Function, the structure factor, and the equation of state of hard-core fluids in fractal dimension d (1≤d≤3) are developed as heuristic interpolations from the knowledge of the exact and Percus-Yevick results for the hard-rod and hard-sphere fluids, respectively. In order to assess their value, such approximate results are compared with those of recent Monte Carlo simulations and numerical solutions of the Percus-Yevick equation for a fractal dimension [M. Heinen et al., Phys. Rev. Lett. 115, 097801 (2015)PRLTAO0031-900710.1103/PhysRevLett.115.097801], a good agreement being observed.
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on the Radial Distribution Function of a hard sphere fluid
Journal of Chemical Physics, 2006Co-Authors: Lopez M De Haro, Andrés Santos, S. Bravo YusteAbstract:Two related approaches, one fairly recent [A. Trokhymchuk et al., J. Chem. Phys.123, 024501 (2005)] and the other one introduced 15years ago [S. B. Yuste and A. Santos, Phys. Rev. A43, 5418 (1991)], for the derivation of analytical forms of the Radial Distribution Function of a fluid of hard spheres are compared. While they share similar starting philosophy, the first one involves the determination of 11 parameters while the second is a simple extension of the solution of the Percus-Yevick equation. It is found that the second approach has a better global accuracy and the further asset of counting already with a successful generalization to mixtures of hard spheres and other related systems.
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on the Radial Distribution Function of a hard sphere fluid
arXiv: Statistical Mechanics, 2006Co-Authors: Lopez M De Haro, Andrés Santos, S. Bravo YusteAbstract:Two related approaches, one fairly recent [A. Trokhymchuk et al., J. Chem. Phys. 123, 024501 (2005)] and the other one introduced fifteen years ago [S. B. Yuste and A. Santos, Phys. Rev. A 43, 5418 (1991)], for the derivation of analytical forms of the Radial Distribution Function of a fluid of hard spheres are compared. While they share similar starting philosophy, the first one involves the determination of eleven parameters while the second is a simple extension of the solution of the Percus-Yevick equation. It is found that the {second} approach has a better global accuracy and the further asset of counting already with a successful generalization to mixtures of hard spheres and other related systems.
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A heuristic Radial Distribution Function for hard disks
The Journal of Chemical Physics, 1993Co-Authors: S. Bravo Yuste, Andrés SantosAbstract:We propose a model Radial Distribution Function for hard disks that is interpolated between the Percus–Yevick Distribution Functions for hard rods and hard spheres. The model contains a mixing parameter and two scaling parameters, which are determined by imposing self‐consistency with an extension to d=2 of the Carnahan–Starling equation of state. Comparison with computer simulation is carried out.
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Radial Distribution Function for hard spheres
Physical Review A, 1991Co-Authors: Bravo S Yuste, Andrés SantosAbstract:The Radial Distribution Function g(r) provided by the solution of the Percus-Yevick (PY) equation for hard spheres is rederived in terms of the simplest Pad\'e approximant of a Function defined in the Laplace space that is consistent with the following physical requirements: g(r) is continuous for rg1, the isothermal compressibility is finite, and the zeroth- and first-order coefficients in the density expansion of g(r) must be exact. An explicit expression for the solution of the generalized mean-spherical approximation (GMSA) is obtained as a simple extension involving two new parameters, which are determined by imposing two conditions: (i) the virial and the compressibility routes to the equation of state agree consistently, and (ii) this equation of state coincides with that of Carnahan and Starling [J. Chem. Phys. 51, 635 (1969)]. The second- and third-order coefficients in the density expansion of g(r) given by the GMSA are compared with the exact ones and with those given by the PY equation.
F. S. Carvalho - One of the best experts on this subject based on the ideXlab platform.
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Radial Distribution Function for liquid gallium from experimental structure factor: a Hopfield neural network approach
Journal of Molecular Modeling, 2020Co-Authors: F. S. Carvalho, J. P. BragaAbstract:Hopfield neural network was used to retrieve liquid gallium Radial Distribution Function from an experimental structure factor, obtained at 959 K. The inversion framework was carried out under two initial conditions: (a) a constant Radial Distribution Function corresponding to an ideal gas and (b) a step Function, simulating a gas with square well potential of interaction. Both situations lead to accurate inverse results if compared with the Radial Distribution Function obtained by Bellisent-Funel et al., using the Fourier transform method and Monte Carlo simulation. The Hopfield neural network has shown to be a powerful strategy to calculate the Radial Distribution Function from experimental data.
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neural network in the inverse problem of liquid argon structure factor from gas to liquid Radial Distribution Function
Theoretical Chemistry Accounts, 2020Co-Authors: F. S. Carvalho, J. P. Braga, M O Alves, C E M GoncalvesAbstract:Within the framework of the inverse problem theory, together with the dynamical Hopfield neural, the Radial Distribution Function of liquid argon was obtained from neutron scattering data. A modest initial condition, the Boltzmann factor for a pair potential or an ideal gas Radial Distribution Function, was used to propagate the neural differential equations. In both cases, the inverted data were obtained with great accuracy. The present work shows that a combination of the inverse theory approach, together with the Hopfield neural network, is a powerful method to obtain liquid properties. Results are obtained almost instantaneously and can be applied for more complex systems.
Y C Chiew - One of the best experts on this subject based on the ideXlab platform.
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Radial Distribution Function of freely jointed hard sphere chains in the solid phase
Journal of Chemical Physics, 2006Co-Authors: T W Cochran, Y C ChiewAbstract:Monte Carlo simulation is used to generate the Radial Distribution Function of freely jointed tangent-bonded hard-sphere chains in the disordered solid phase for chain lengths of three, four, six, and eight segments. The data are used to create an accurate analytical expression of the total Radial Distribution Function of the hard-sphere chains that covers a density range from the solidification point up to a packing fraction of 0.71. It is envisioned that the correlation will help further progress toward molecular thermodynamic treatment of the solid phase in general and toward perturbed chain theories for the solid phase, in particular.
C E M Goncalves - One of the best experts on this subject based on the ideXlab platform.
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neural network in the inverse problem of liquid argon structure factor from gas to liquid Radial Distribution Function
Theoretical Chemistry Accounts, 2020Co-Authors: F. S. Carvalho, J. P. Braga, M O Alves, C E M GoncalvesAbstract:Within the framework of the inverse problem theory, together with the dynamical Hopfield neural, the Radial Distribution Function of liquid argon was obtained from neutron scattering data. A modest initial condition, the Boltzmann factor for a pair potential or an ideal gas Radial Distribution Function, was used to propagate the neural differential equations. In both cases, the inverted data were obtained with great accuracy. The present work shows that a combination of the inverse theory approach, together with the Hopfield neural network, is a powerful method to obtain liquid properties. Results are obtained almost instantaneously and can be applied for more complex systems.
