Fluid molecular dynamics simulations

The correct treatment of boundaries and boundary effects is crucial to simulation methods because it enables macroscopic properties to be calculated from simulations using relatively small numbers of particles. The importance of boundary effects can be illustrated by considering the following simple example. Suppose we have a cube of volume 1 litre which is filled with water at room temperature. The cube contains approximately 3.3 X 10 molecules. Interactions with the walls can extend up to 10 molecular diameters into the fluid. The diameter of the water molecule is approximately 2.8 A and so the number of water molecules that are interacting with the boundary is about 2 x 10. So only about one in 1.5 million water molecules is influenced by interactions with the walls of the container. The number of particles in a Monte Carlo or molecular dynamics simulation is far fewer than 10 -10 and is frequently less than 1000. In a system of 1000 water molecules most, if not all of them, would be within the influence of the walls of the boundary. Clecirly, a simulation of 1000 water molecules in a vessel would not be an appropriate way to derive bulk properties. The alternative is to dispense with the container altogether. Now, approximately three-quarters of the molecules would be at the surface of the sample rather than being in the bulk. Such a situation would be relevcUit to studies of liquid drops, but not to studies of bulk phenomena. 

For any even vaguely realistic atomically constituted membrane it is unlikely that any theory will become available in the near future which will properly or reasonably describe the dynamic properties of the membrane, the fluids near it, and their passage, or selective passage, through it. Nevertheless, one should continue trying with simple models and simple theories [39-43], which show the way forward and can, as usual, be tested by the virtually exact results of molecular dynamics simulation. 

R. L. Rowley, M. Henrichsen. Calculation of chemical potential for structured molecules using osmotic molecular dynamics simulations. Fluid Phase Equil 137 15, 1997. 

In contrast to the single molecule case, Monte Carlo methods tend to be rather less efficient than molecular dynamics in sampling phase space for a bulk fluid. Consequently, most of the bulk simulations of liquid crystals described in Sect. 5.1 use molecular dynamics simulation methods. 

Figure 5. Molecular dynamics simulation of the decay forward and backward in time of the fluctuation of the first energy moment of a Lennard-Jones fluid (the central curve is the average moment, the enveloping curves are estimated standard error, and the lines are best fits). The starting positions of the adiabatic trajectories are obtained from Monte Carlo sampling of the static probability distribution, Eq. (246). The density is 0.80, the temperature is Tq — 2, and the initial imposed thermal gradient is pj — 0.02. (From Ref. 2.)...

Moller, D. Fischer, J., Vapour liquid equilibrium of a pure fluid from test particle method in combination with npt molecular dynamics simulations, Mol. Phys. 1990, 69, 463 173... 

To simulate the particle-particle collision, the hard-sphere model, which is based on the conservation law for linear momentum and angular momentum, is used. Two empirical parameters, a restitution coefficient of 0.9 and a friction coefficient of 0.3, are utilized in the simulation. In this study, collisions between spherical particles are assumed to be binary and quasi-instantaneous. The equations, which follow those of molecular dynamic simulation, are used to locate the minimum flight time of particles before any collision. Compared with the soft-sphere particle-particle collision model, the hard-sphere model accounts for the rotational particle motion in the collision dynamics calculation thus, only the translational motion equation is required to describe the fluid induced particle motion. In addition, the hard-sphere model also permits larger time steps in the calculation therefore, the simulation of a sequence of collisions can be more computationally effective. The details of this approach can be found in the literature (Hoomans et al., 1996 Crowe et al., 1998). 

Chiu, S. W., Clark, M., Balaji, V., Subramaniam, S., Scott, H. L. and Jakobsson, E. (1995). Incorporation of surface tension into molecular dynamics simulation of an interface a fluid phase lipid bilayer membrane, Biophys. J., 69,1230-1245. 

Berger, O., Edholm, O. and Jahnig F. (1997). Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure and constant temperature, Biophys. J., 72, 2002-2013. 

Figure 8 Compressibility factor P/fiksT versus density p = pa3 of the hard-sphere system as calculated from both free-volume information (Eq. [8]) and the collision rate measured in molecular dynamics simulations. The empirically successful Camahan-Starling84 equation of state for the hard-sphere fluid is also shown for comparison. (Adapted from Ref. 71).

From this, the velocities of particles flowing near the wall can be characterized. However, the absorption parameter a must be determined empirically. Sokhan et al. [48, 63] used this model in nonequilibrium molecular dynamics simulations to describe boundary conditions for fluid flow in carbon nanopores and nanotubes under Poiseuille flow. The authors found slip length of 3nm for the nanopores [48] and 4-8 nm for the nanotubes [63]. However, in the first case, a single factor [4] was used to model fluid-solid interactions, whereas in the second, a many-body potential was used, which, while it may be more accurate, is significantly more computationally intensive. 

Here ji(qa) is the spherical Bessel function of order l,g(a) is the radial distribution function at contact, and f = /fSmn/Anpo2g a) is the Enskog mean free time between collisions. The transport coefficients in the above expressions are given only by their Enskog values that is, only collisional contributions are retained. Since it is only in dense fluids that the Enskog values represents the important contributions to transport coefficient, the above expressions are reasonable only for dense hard-sphere fluids. Earlier Alley, Alder, and Yip [32] have done molecular dynamics simulations to determine the wavenumber-dependent transport coefficients that should be used in hard-sphere generalized hydrodynamic equations. They have shown that for intermediate values of q, the wavenumber-dependent transport coefficients are well-approximated by their collisional contributions. This implies that Eqs. (20)-(23) are even more realistic as q and z are increased. 

Substantial evidence suggests that in highly asymmetric supercritical mixtures the local and bulk environment of a solute molecule differ appreciably. The concept of a local density enhancement around a solute molecule is supported by spectroscopic, theoretical, and computational investigations of intermolecular interactions in supercritical solutions. Here we make for the first time direct comparison between local density enhancements determined for the system pyrene in CO2 by two very different methods-fluorescence spectroscopy and molecular dynamics simulation. The qualitative agreement is quite satisfactory, and the results show great promise for an improved understanding at a molecular level of supercritical fluid solutions. 

Roccatano D, Amadei A, Apol MEF, Nola AD, Berendsen HJC (1998) Application of the quasi-Gaussian entropy theory to molecular dynamic simulation of Lennard-jones fluids, J Chem Phys, 109 6358-6363... 

Figure 1.38. Molecular dynamics simulation of the density profiles for spherical molecules in a cylinder, mimicking SFg in controlled pore glass (CPG-10). Fluid-fluid and fluid-wall interaction modelled by Lennard-Jones interactions. Reference A. de Keizer. T. Michalski and G.H. Findenegg, Pure Appl. Chem. 63(1991) 1495.

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