Long time trajectories, molecular dynamics

We may look at atomic clusters as particularly apt and useful models to study virtually every aspect of what we call, diffusely, complexity. Simulations are particularly powerful means to carry out such studies. For example, we can follow trajectories for very long times with molecular dynamics and thereby evaluate the global means of the exponential rates of divergence of neighboring trajectories. This is the most common way to evaluate those exponents, the Liapunov exponents. The sum of these is the Kolmogorov entropy, one gross measure of the volume of phase space that the system explores, and hence one... 

An extremely valuable property of molecular dynamics is that, in principle, this technique can describe a real physical evolutionary process. This cannot be done by statistical sampling techniques, as e.g. Monte Carlo, because they do not calculate physical evolution. However, as we have seen from the preceding discussion of the MD trajectory, on a sufficiently long time scale, molecular dynamics is in fact a statistical sampling technique. Does this mean that molecular dynamics has to give up its claim of being able to calculate real evolutionary processes To address this question, we need to recognize that a phase-space trajectory carries two classes of information, namely microscopic and macroscopic. Suppose the microscopic state of the system, i.e. the precise phase-space point, is known at t = 0. This information is lost by an actual MD calculation, in a time order of which is the... 

As a consequence of this observation, the essential dynamics of the molecular process could as well be modelled by probabilities describing mean durations of stay within different conformations of the system. This idea is not new, cf. [10]. Even the phrase essential dynamics has already been coined in [2] it has been chosen for the reformulation of molecular motion in terms of its almost invariant degrees of freedom. But unlike the former approaches, which aim in the same direction, we herein advocate a different line of method we suggest to directly attack the computation of the conformations and their stability time spans, which means some global approach clearly differing from any kind of statistical analysis based on long term trajectories. 

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. 

Successful molecular dynamics simulations should have a fairly stable trajectory. Instability and lack of ec uilibratioii can result from a large time step, treatment of long-range cutoffs, or unrealistic coiiplin g to a temperature bath. ... 

In our last example we return to the issue of the possible damaging effects of the standard geometry constraints. Two long trajectories have been computed for a partially hydrated dodecamer DNA duplex of the previous example, first by using ICMD and second with Cartesian coordinate molecular dynamics without constraints [54]. Both trajectories started from the same initial conformation with RMSD of 2.6 A from the canonical B-DNA form. Figure 5 shows the time evolution of RMSD from the canonical A and B conformations. Each point in the figure corresponds to a 15 ps interval and shows an average RMSD value. We see that both trajectories approach the canonical B-DNA, while the RMSD... 

The highest probability paths will make the argument of the exponential small. That will be true for paths that follow Newtonian dynamics where mr = F(r). Olender and Elber [45] demonstrated how large values of the time step ht can be used in a way that projects out high frequency motions of the system and allows for the simulation of long-time molecular dynamics trajectories for macromolecular systems. 

There are two important consequences of this equality for computer simulations of many-body systems. First, it means that statistically averaged properties of these systems are accessible from simulations that are aimed at generating trajectories -e.g., molecular dynamics, or ensemble averages such as Monte Carlo. Furthermore, for sufficiently long trajectories, the time-averaged properties become independent of the initial conditions. Stated differently, it means that for almost all values of qo, Po, the system will pass arbitrarily close to any point x, p, in phase space at some later time. 

The strategy in a molecular dynamics simulation is conceptually fairly simple. The first step is to consider a set of molecules. Then it is necessary to choose initial positions of all atoms, such that they do not physically overlap, and that all bonds between the atoms have a reasonable length. Subsequently, it is necessary to specify the initial velocities of all the atoms. The velocities must preferably be consistent with the temperature in the system. Finally, and most importantly, it is necessary to define the force-field parameters. In effect the force field defines the potential energy of each atom. This value is a complicated sum of many contributions that can be computed when the distances of a given atom to all other atoms in the system are known. In the simulation, the spatial evolution as well as the velocity evolution of all molecules is found by solving the classical Newton equations of mechanics. The basic outcome of the simulation comprises the coordinates and velocities of all atoms as a function of the time. Thus, structural information, such as lipid conformations or membrane thickness, is readily available. Thermodynamic information is more expensive to obtain, but in principle this can be extracted from a long simulation trajectory. 

We have outlined a new numerical approach to compute approximate long-time molecular dynamics trajectories. We have explained the underlying assumptions and the limitations of the present approach as well as its promise. Numerical examples were shown for relatively small system for which detailed and extensive calculations can be performed. A future direction, the calculation of relative rates, was outlined. The research described in this chapter was supported by grants from the NIH GM59796 and the NSF Grant No. 9982524 to Ron Elber. 

The extended simple point charge (SPC/E) model [59] is used. This model is known to give reasonably accurate values of static dielectric permittivity of liquid water at ambient conditions [60]. The MD simulations were performed for both H2O and D2O with the system size of 1024 particles at 220 K, 240 K, 267 K, 273 K, 300 K, and 355 K. The parallel molecular dynamics code for arbitrary molecular mixtures (DynaMix) is implemented by Lyubartsev and Laaksonen [61]. The simulations have been carried out on a Linux cluster built on the Tyan/Opteron 64 platform, which enables calculations of relatively long trajectories for a system of 1024 water molecules. The simulation run lengths depend on temperature and are in the range between 1 ns and 4 ns for the warmest and coldest simulation, respectively. As the initial condition was a cubic lattice, the equilibration time was chosen to be temperature dependent in the range from 200 ps at 355 Ktol ns at 200K. 

One of the biggest limitations of current molecular dynamics calculations is that trajectories can only be followed for around 100 ps with reasonable computation times. This short time scale complicates the observation of activated phenomena and makes the method sensitive to the choice of initial conditions, since the simulation may not run long enough to adequately sample configuration space. 100 ps is far too short to model dynamics that affect the NMR lineshape, for which the time scale is typically on the order of a ms. As demonstrated by Thomas and coworkers 132 and others, hybrid approaches employing Monte Carlo methods and other methods in addition to molecular dynamics can extend these simulations to longer effective time scales. 

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