Molecular dynamics simulation dynamical properties

To end this section and the review, we mention briefly the first results from the simulation on laboratory-frame cross-correlation of the type (v(f)J (0)). Here v is the molecular center-of-mass linear velocity and J is the molecular angular momentum in the usual laboratory frame of reference. For chiral molecules the center-of-mass linear velocity v seems to be correlated directly in the laboratory frame with the molecule s own angular momentum J at different points r in the time evolution of the molectilar ensemble. This is true in both the presence and absence of an external electric field. These results illustrate the first direct observation of elements of (v(r)J (0)) in the laboratory frame of reference. The racemic modification of physical and molecular dynamical properties depends, therefore, on the theorem (v(r)J (0)) 0 in both static and moving frames of reference. An external electric field enhances considerably the magnitude of the cross-correlations. 

Numerical methods with different time- and length scales are employed and developed to investigate material properties and behaviors. Among them, molecular modeling can predict the molecular behaviors and correlate macroscopic properties of a material with various variables. The most popular techniques include molecular mechanics (MM), MD, and Monte Carlo (MC) simulation. These techniques are now routinely used to investigate the structure, dynamics, and thermodynamics of inorganic, biological, and polymer systems. They have recently been used to predict the thermodynamic and kinetic properties of nanoparticle-matrix mixtures, interfacial molecular structure and interactions, molecular dynamic properties, and mechanical properties. 

Two simulation methods—Monte Carlo and molecular dynamics—allow calculation of the density profile and pressure difference of Eq. III-44 across the vapor-liquid interface [64, 65]. In the former method, the initial system consists of N molecules in assumed positions. An intermolecule potential function is chosen, such as the Lennard-Jones potential, and the positions are randomly varied until the energy of the system is at a minimum. The resulting configuration is taken to be the equilibrium one. In the molecular dynamics approach, the N molecules are given initial positions and velocities and the equations of motion are solved to follow the ensuing collisions until the set shows constant time-average thermodynamic properties. Both methods are computer intensive yet widely used. 

Small metal clusters are also of interest because of their importance in catalysis. Despite the fact that small clusters should consist of mostly surface atoms, measurement of the photon ionization threshold for Hg clusters suggest that a transition from van der Waals to metallic properties occurs in the range of 20-70 atoms per cluster [88] and near-bulk magnetic properties are expected for Ni, Pd, and Pt clusters of only 13 atoms [89] Theoretical calculations on Sin and other semiconductors predict that the stmcture reflects the bulk lattice for 1000 atoms but the bulk electronic wave functions are not obtained [90]. Bartell and co-workers [91] study beams of molecular clusters with electron dirfraction and molecular dynamics simulations and find new phases not observed in the bulk. Bulk models appear to be valid for their clusters of several thousand atoms (see Section IX-3). 

It is possible to use the quantum states to predict the electronic properties of the melt. A typical procedure is to implement molecular dynamics simulations for the liquid, which pemiit the wavefiinctions to be detemiined at each time step of the simulation. As an example, one can use the eigenpairs for a given atomic configuration to calculate the optical conductivity. The real part of tire conductivity can be expressed as... 

The alternative simulation approaches are based on molecular dynamics calculations. This is conceptually simpler that the Monte Carlo method the equations of motion are solved for a system of A molecules, and periodic boundary conditions are again imposed. This method pennits both the equilibrium and transport properties of the system to be evaluated, essentially by numerically solvmg the equations of motion... 

The most important molecular interactions of all are those that take place in liquid water. For many years, chemists have worked to model liquid water, using molecular dynamics and Monte Carlo simulations. Until relatively recently, however, all such work was done using effective potentials [4T], designed to reproduce the condensed-phase properties but with no serious claim to represent the tme interactions between a pair of water molecules. 

The complexity of polymeric systems make tire development of an analytical model to predict tlieir stmctural and dynamical properties difficult. Therefore, numerical computer simulations of polymers are widely used to bridge tire gap between tire tlieoretical concepts and the experimental results. Computer simulations can also help tire prediction of material properties and provide detailed insights into tire behaviour of polymer systems. A simulation is based on two elements a more or less detailed model of tire polymer and a related force field which allows tire calculation of tire energy and tire motion of tire system using molecular mechanisms, molecular dynamics, or Monte Carlo teclmiques 1631. 

Teleman, O. An efficient way to conserve the total energy in molecular dynamics simulations boundary effects on energy conservation and dynamic properties. Mol. Simul. 1 (1988) 345-355. 

Zigmond, 1988). The ATP-hydrolysis that accompanies actin polymerization, ATP —> ADP + Pj, and the subsequent release of the cleaved phosphate (Pj) are believed to act as a clock (Pollard et ah, 1992 Allen et ah, 1996), altering in a time-dependent manner the mechanical properties of the filament and its propensity to depolymerize. Molecular dynamics simulations suggested a so-called back door mechanism for the hydrolysis reaction ATP ADP - - Pj in which ATP enters the actin from one side, ADP leaves from the same side, but Pj leaves from the opposite side, the back door (Wriggers and Schulten, 1997b). This hypothesis can explain the effect of the toxin phalloidin which blocks the exit of the putative back door pathway and, thereby, delays Pi release as observed experimentally (Dancker and Hess, 1990). 

Abstract. Molecular dynamics (MD) simulations of proteins provide descriptions of atomic motions, which allow to relate observable properties of proteins to microscopic processes. Unfortunately, such MD simulations require an enormous amount of computer time and, therefore, are limited to time scales of nanoseconds. We describe first a fast multiple time step structure adapted multipole method (FA-MUSAMM) to speed up the evaluation of the computationally most demanding Coulomb interactions in solvated protein models, secondly an application of this method aiming at a microscopic understanding of single molecule atomic force microscopy experiments, and, thirdly, a new method to predict slow conformational motions at microsecond time scales. 

It is appropriate to consider first the question of what kind of accuracy is expected from a simulation. In molecular dynamics (MD) very small perturbations to initial conditions grow exponentially in time until they completely overwhelm the trajectory itself. Hence, it is inappropriate to expect that accurate trajectories be computed for more than a short time interval. Rather it is expected only that the trajectories have the correct statistical properties, which is sensible if, for example, the initial velocities are randomly generated from a Maxwell distribution. 

Among the main theoretical methods of investigation of the dynamic properties of macromolecules are molecular dynamics (MD) simulations and harmonic analysis. MD simulation is a technique in which the classical equation of motion for all atoms of a molecule is integrated over a finite period of time. Harmonic analysis is a direct way of analyzing vibrational motions. Harmonicity of the potential function is a basic assumption in the normal mode approximation used in harmonic analysis. This is known to be inadequate in the case of biological macromolecules, such as proteins, because anharmonic effects, which MD has shown to be important in protein motion, are neglected [1, 2, 3]. 

The principal idea behind the CSP approach is to use input from Classical Molecular Dynamics simulations, carried out for the process of interest as a first preliminary step, in order to simplify a quantum mechanical calculation, implemented in a subsequent, second step. This takes advantage of the fact that classical dynamics offers a reasonable description of many properties of molecular systems, in particular of average quantities. More specifically, the method uses classical MD simulations in order to determine effective... 

The visualization of volumetric properties is more important in other scientific disciplines (e.g., computer tomography in medicine, or convection streams in geology). However, there are also some applications in chemistry (Figure 2-125d), among which only the distribution of water density in molecular dynamics simulations will be mentioned here. Computer visualization of this property is usually realized with two or three dimensional textures [203]. 

The explicit definition of water molecules seems to be the best way to represent the bulk properties of the solvent correctly. If only a thin layer of explicitly defined solvent molecules is used (due to hmited computational resources), difficulties may rise to reproduce the bulk behavior of water, especially near the border with the vacuum. Even with the definition of a full solvent environment the results depend on the model used for this purpose. In the relative simple case of TIP3P and SPC, which are widely and successfully used, the atoms of the water molecule have fixed charges and fixed relative orientation. Even without internal motions and the charge polarization ability, TIP3P reproduces the bulk properties of water quite well. For a further discussion of other available solvent models, readers are referred to Chapter VII, Section 1.3.2 of the Handbook. Unfortunately, the more sophisticated the water models are (to reproduce the physical properties and thermodynamics of this outstanding solvent correctly), the more impractical they are for being used within molecular dynamics simulations. 

A sequence of successive con figurations from a Mon te Carlo simulation constitutes a trajectory in phase space with IlypcrC hem. this trajectory in ay be saved and played back in the same way as a dynamics trajectory. With appropriate choices of setup parameters, the Mon te Carlo m ethod m ay ach leve ec nilibration more rapidly than molecular dynamics. Tor some systems, then. Monte C arlo provides a more direct route to equilibrium sinictural and thermodynamic properties. However, these calculations can be quite long, depentiing upon the system studied. 

One drawback to a molecular dynamics simulation is that the trajectory length calculated in a reasonable time is several orders of magnitude shorter than any chemical process and most physical processes, which occur in nanoseconds or longer. This allows yon to study properties that change w ithin shorter time periods (such as energy finctnations and atomic positions), but not long-term processes like protein folding. 

In many molecular dynamics simulations, equilibration is a separate step that precedes data collection. Equilibration is generally necessary lo avoid introducing artifacts during the healing step an d to en su re th at the trajectory is aciii ally sim u laiin g eq u i librium properties. The period required for equilibration depends on the property of Interest and the molecular system. It may take about 100 ps for the system to approach equilibrium, but some properties are fairly stable after 1 0-20 ps. Suggested tim es range from. 5 ps to nearly 100 ps for medium-si/ed proteins. 

In some cases the atomic charges are chosen to reproduce thermodynamic properties calculated using a molecular dynamics or Monte Carlo simulation. A series of simulations is performed and the charge model is modified until satisfactory agreement with experiment is obtained. This approach can be quite powerful despite its apparent simplicity, but it is only really practical for small molecules or simple models. 

The thermodynamic properties that we have considered so far, such as the internal energy, the pressure and the heat capacity are collectively known as the mechanical properties and can be routinely obtained from a Monte Carlo or molecular dynamics simulation. Other thermodynamic properties are difficult to determine accurately without resorting to special techniques. These are the so-called entropic or thermal properties the free energy, the chemical potential and the entropy itself. The difference between the mechanical emd thermal properties is that the mechanical properties are related to the derivative of the partition function whereas the thermal properties are directly related to the partition function itself. To illustrate the difference between these two classes of properties, let us consider the internal energy, U, and the Fielmholtz free energy, A. These are related to the partition function by ... 

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. 

Calculating the statistical efficiency, a. A plot of tj,a A)i,la A) against tj, shows a steep rise before j off. Here the property A corresponds to the pressure calculated from the molecular dynamics simulation of... 

A molecular dynamics simulation provides data values at specific times. This enables thi value of some property at some instant to be correlated with the value of the same or anothe property at a later time t. The resulting values are known as time correlation coefficients. Thi correlation function is then written ... 

---