Comparison of radial distribution function for silica glass with those for various bonding topologies: Use of correlation function

1982 ◽  
Vol 53 (1-2) ◽  
pp. 135-141 ◽  
Author(s):  
J.H. Konnert ◽  
P. D'Antonio ◽  
J. Karle
Keyword(s):  
Distribution Function ◽  
Correlation Function ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Silica Glass

scholarly journals Correlation between structure characteristics and pair radial distribution function in Silica glass under compression

2018 ◽  
Vol 34 (4) ◽  
Author(s):  
Lan Mai Thi
Keyword(s):  
High Pressure ◽  
Distribution Function ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Silica Glass ◽  
Structural Phase ◽  
Dynamics Simulation ◽  
Pairwise Interactions ◽  
Fcc Structure ◽  
Second Peak

We have studied structure of silica glass at different pressures and temperature of 300K by using Molecular Dynamics simulation (MD) method. The model consists of 6000 atoms (2000 Si, 4000 O atoms) with the periodic boundary condition. We applied the Morse-Stretch potentials which describe the pairwise interactions between ions for SiO2 system. There is structural phase transformation from tetrahedra (SiO4) to octahedra (SiO6) network structure. There is splitting in the Si-Si pair radial distribution function (PRDF) at high pressure (100 GPa). The original of this splitting relates to the edge- and face-sharing bonds. The new second peak of the O-O PRDF at the high pressure originates from oxygen atoms of the edge-sharing bonds. Thus, there is rearrangement of O atoms. O atoms have tendency to more order arrangement that leads to form some oxygen hcp and fcc structure in the model at high pressure.

ON THE TWO-POINT CORRELATION FUNCTION FOR DISPERSIONS OF NONOVERLAPPING SPHERES

1998 ◽  
Vol 08 (02) ◽  
pp. 359-377 ◽  
Author(s):  
KONSTANTIN Z. MARKOV ◽  
JOHN R. WILLIS
Keyword(s):  
Distribution Function ◽  
Correlation Function ◽  
Characteristic Function ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Integral Formula ◽  
Probability Densities ◽  
Point Correlation ◽  
Point Correlation Function ◽  
Point Level

Random dispersions of spheres are useful and appropriate models for a wide class of particulate random materials. They can be described in two equivalent and alternative ways — either by the multipoint moments of the characteristic function of the region, occupied by the spheres, or by the probability densities of the spheres' centers. On the "two-point" level, a simple and convenient integral formula is derived which interconnects the radial distribution function of the spheres with the two-point correlation of the said characteristic function. As one of the possible applications of the formula, the behavior of the correlation function near the origin is studied in more detail and related to the behavior of the radial distribution function at the "touching" separation of the spheres.

SQUARE-WELL FLUID AT LOW DENSITIES

1967 ◽  
Vol 45 (12) ◽  
pp. 3959-3978 ◽  
Author(s):  
J. A. Barker ◽  
D. Henderson
Keyword(s):  
Distribution Function ◽  
Correlation Function ◽  
Critical Temperature ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Virial Coefficients ◽  
Hard Core ◽  
Direct Correlation Function ◽  
Virial Series ◽  
Square Well

Values for the radial distribution function and the direct correlation function at low densities and for the first five virial coefficients are obtained for a fluid of molecules interacting according to the square-well potential when the width of the attractive well is half the radius of the hard core. It is found that the higher-order coefficients are surprisingly large and, as a result, the virial series fails to converge even at temperatures and volumes significantly greater than the critical temperature and volume. Comparisons of these exact virial coefficients with those given by several approximate theories are made. Values are also given for the first five virial coefficients when the width of the attractive well is equal to the radius of the hard core.

How to analyse the results

2017 ◽  
Author(s):  
Michael P. Allen ◽  
Dominic J. Tildesley
Keyword(s):  
Distribution Function ◽  
Correlation Function ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Transport Coefficients ◽  
Time Correlation ◽  
Time Correlation Function ◽  
Einstein Relation ◽  
Time Correlation Functions ◽  
Practical Guidance

In this chapter, practical guidance is given on the calculation of thermodynamic, structural, and dynamical quantities from simulation trajectories. Program examples are provided to illustrate the calculation of the radial distribution function and a time correlation function using the direct and fast Fourier transform methods. There is a detailed discussion of the calculation of statistical errors through the statistical inefficiency. The estimation of the error in equilibrium averages, fluctuations and in time correlation functions is discussed. The correction of thermodynamic averages to neighbouring state points is described along with the extension and extrapolation of the radial distribution function. The calculation of transport coefficients by the integration of the time correlation function and through the Einstein relation is discussed.

The Influence of Hydrogen atoms on the Performance of Radial Distribution Function Based Descriptors in the Chemoinformatic Studies of HIV-1 Prote-ase Complexes with Inhibitors.

2020 ◽  
Vol 17 ◽  
Author(s):  
Jurica Novak ◽  
Maria A. Grishina ◽  
Vladimir A. Potemkin
Keyword(s):  
Distribution Function ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Current Model ◽  
Direct Interaction ◽  
Hydroxyl Group ◽  
Linear Regression Method ◽  
Hydrogen Atoms ◽  
Protease Enzyme ◽  
Hiv 1

: In this letter the newly introduced approach based on the radial distribution function (RDF) weighted by the number of va-lence shell electrons is applied for a series of HIV-1 protease enzyme and its complexes with inhibitors to evaluate the influ-ence of hydrogen atoms on the performance of the model. The multiple linear regression method was used for the selection of the relevant descriptors. Two groups of residues having dominant contribution to the RDF descriptor are identified as relevant for the inhibition. In the first group are residues like Arg8, Asp25, Thr26, Gly27 and Asp29, which establish direct interaction with the inhibitor, while the second group consists of the amino acids at the interface of the two homodimer sub-units or with the solvent. The crucial motif pointed out by our approach as the most important for inhibition of the enzyme’s activity and present in all inhibitors is hydroxyl group that establish hydrogen bond with Asp25 side chain. Additionally, the comparison to the model without hydrogen showed that both models are of similar quality, but the downside of the current model is the need for the determination of residues’ protonation states.

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