Particles molecular dynamics

Rapaport D C 1991 Multi-million particle molecular dynamics processing Comput. Phys. Commun. 62 217-28... 

D. C. Rapaport, Comput. Phys. Commun., 62, 198 (1991). Multi-Million Particle Molecular Dynamics. I. Design Considerations for Vector Processing. 

P. S. Lomdahl and D. M. Beazly, Multi-Million Particle Molecular Dynamics in MPPs, in Applied Parallel Computing, Computations in Physics, Chemistry and Engineering Science, J. Dongarra, K. Madsen, and J. Wasniewski, eds., vol. 1041 of Lect. Notes Comput. Sci., Springer-Verlag, 1996, 391 07. 

R. W. Hockney, S. P. Goel, and C. R. Eastwood, A 10000 particle molecular dynamics model with long range forces, Chem. Phys. Lett., 21 (1973), 589-591. 

Swope, W.C., and Anderson, H.C. (1990) 106-Particle Molecular-Dynamics Study of Homogeneous Nucleation of Crystals in a Supercooled Atomic Liquid, Phys. Rev. B, vol. 41, pp.7042-7054. 

W. C. Swope and H. C. Andersen (1990) 10(6)-Particle molecular-dynamics study of homogeneous nucleation of crystals in a supercooled atomic hquid. Phys. Rev. B 41, pp. 7042-7054... 

Figure 1. Famous early simulation results showing the existence of extended metastable behavior of two phases in a finite system of hard spheres. The heavy curves and plus signs (+) represent 108 and 32 particle molecular dynamics data of Alder and Wainwright the circles are MC data due to Wood and Jacobson (F0 is the close-packed volume). [Reprinted with permission from W. W. Wood and J. D. Jacobson, J. Chem. Phys. 27,1207 (1957).]...

Figure 6.12 The second invariant q2 of the distance matrix D plotted versus the packing fraction cf) for N = 6 bidisperse disk packings with -N = 1 (first-order saddles) generated via the basic" Monte Carlo method (black dots) and soft-particle molecular dynamics simulations of frictional particles with static friction coefficient p = 0.002 (triangles), 0.02 (squares), and 0.2 (crosses). MS packings for frictionless bidisperse disks with N -Nc = 0 are indicated by filled circles.

If we wish to know the number of (VpV)-collisions that actually take place in this small time interval, we need to know exactly where each particle is located and then follow the motion of all the particles from time tto time t+ bt. In fact, this is what is done in computer simulated molecular dynamics. We wish to avoid this exact specification of the particle trajectories, and instead carry out a plausible argument for the computation of r To do this, Boltzmann made the following assumption, called the Stosszahlansatz, which we encountered already in the calculation of the mean free path ... 

The projector augmented-wave (PAW) DFT method was invented by Blochl to generalize both the pseudopotential and the LAPW DFT teclmiques [M]- PAW, however, provides all-electron one-particle wavefiinctions not accessible with the pseudopotential approach. The central idea of the PAW is to express the all-electron quantities in tenns of a pseudo-wavefiinction (easily expanded in plane waves) tenn that describes mterstitial contributions well, and one-centre corrections expanded in tenns of atom-centred fiinctions, that allow for the recovery of the all-electron quantities. The LAPW method is a special case of the PAW method and the pseudopotential fonnalism is obtained by an approximation. Comparisons of the PAW method to other all-electron methods show an accuracy similar to the FLAPW results and an efficiency comparable to plane wave pseudopotential calculations [, ]. PAW is also fonnulated to carry out DFT dynamics, where the forces on nuclei and wavefiinctions are calculated from the PAW wavefiinctions. (Another all-electron DFT molecular dynamics teclmique using a mixed-basis approach is applied in [84].)... 

The parameter /r tunes the stiffness of the potential. It is chosen such that the repulsive part of the Leimard-Jones potential makes a crossing of bonds highly improbable (e.g., k= 30). This off-lattice model has a rather realistic equation of state and reproduces many experimental features of polymer solutions. Due to the attractive interactions the model exhibits a liquid-vapour coexistence, and an isolated chain undergoes a transition from a self-avoiding walk at high temperatures to a collapsed globule at low temperatures. Since all interactions are continuous, the model is tractable by Monte Carlo simulations as well as by molecular dynamics. Generalizations of the Leimard-Jones potential to anisotropic pair interactions are available e.g., the Gay-Beme potential [29]. This latter potential has been employed to study non-spherical particles that possibly fomi liquid crystalline phases. 

In favourable contrast to molecular dynamics, BD allows molecular movements of realistically long duration to be simulated. Nevertheless, the practical number of protein molecules which can be simulated is only two since collective phenomena are often of crucial importance in detennining the course of interaction events, other simulation teclmiques, such as cellular automata [115], need to be used to capture the behaviour of large numbers of particles. 

In this section, the basic theory of molecular dynamics is presented. Starting from the BO approximation to the nuclear Schrddinger equation, the picture of nuclear dynamics is that of an evolving wavepacket. As this picture may be unusual to readers used to thinking about nuclei as classical particles, a few prototypical examples are shown. 

Molecular dynamics simulations ([McCammon and Harvey 1987]) propagate an atomistic system by iteratively solving Newton s equation of motion for each atomic particle. Due to computational constraints, simulations can only be extended to a typical time scale of 1 ns currently, and conformational transitions such as protein domains movements are unlikely to be observed. 

As is well known. Molecular Dynamics is used to simulate the motions in many-body systems. In a typical MD simulation one first starts with an initial state of an N particle system F = xi,..., Xf,pi,..., pf) where / = 3N is the number of degrees of freedom in the system. After sampling the initial state one numerically solves Hamilton s equations of motion ... 

Parallel molecular dynamics codes are distinguished by their methods of dividing the force evaluation workload among the processors (or nodes). The force evaluation is naturally divided into bonded terms, approximating the effects of covalent bonds and involving up to four nearby atoms, and pairwise nonbonded terms, which account for the electrostatic, dispersive, and electronic repulsion interactions between atoms that are not covalently bonded. The nonbonded forces involve interactions between all pairs of particles in the system and hence require time proportional to the square of the number of atoms. Even when neglected outside of a cutoff, nonbonded force evaluations represent the vast majority of work involved in a molecular dynamics simulation. 

Molecular dynamics (MD) studies the time evolution of N interacting particles via the solution of classical Newton s equations of motion. 

Although, the notion of molecular dynamics was known in the early turn of the century, the first conscious effort in the use of computer for molecular dynamics simulation was made by Alder and Wainright, who in their paper [1] reported the application of molecular dynamics to realistic particle systems. Using hard spheres potential and fastest computers at the time, they were able to simulate systems of 32 to 108 atoms in 10 to 30 hours. Since the work of Alder and Wainright, interests in MD have increased tremendously, see... 

Molecular dynamics conceptually involves two phases, namely, the force calculations and the numerical integration of the equations of motion. In the first phase, force interactions among particles based on the negative gradient of the potential energy function U,... 

Molecular dynamics simulations can produce trajectories (a time series of structural snapshots) which correspond to different statistical ensembles. In the simplest case, when the number of particles N (atoms in the system), the volume V,... 

In periodic boimdary conditions, one possible way to avoid truncation of electrostatic interaction is to apply the so-called Particle Mesh Ewald (PME) method, which follows the Ewald summation method of calculating the electrostatic energy for a number of charges [27]. It was first devised by Ewald in 1921 to study the energetics of ionic crystals [28]. PME has been widely used for highly polar or charged systems. York and Darden applied the PME method already in 1994 to simulate a crystal of the bovine pancreatic trypsin inhibitor (BPTI) by molecular dynamics [29]. 

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. 

Cheatham T E III, J L Miller, T Fox, T A Darden and P A Kollman 1995. Molecular Dynamics Simulations on Solvated Biomolecular Systems The Particle Mesh Ewald Method Leads to Stable Trajectories of DNA, RNA and Proteins. Journal of the American Chemical Society 117 4193-4194. 

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