Molecular Dynamics with Continuous Potentials

Finite difference techniques are used to generate molecular dynamics trajectories with continuous potential models, which we will assume to be pairwise additive. The essential idea is that the integration is broken down into many small stages, each separated in time by a fixed time fit. The total force on each particle in the configuration at a time t is 

The force is assumed to be constant during the time step. The forces on the particles in 

There are many algorithms for integrating the equations of motion using finite difference methods, several of which are commonly used in molecular dynamics calculations. All algorithms assume that the positions and dynamic properties (velocities, accelerations, etc.) can be approximated as Taylor series expansions  

The velocities do not explicitly appear in the Verlet integration algorithm. The velocities can be calculated in a variety of ways a simple approach is to divide the difference in positions at times t + St and t — St by 2St  

Several variations on the Verlet algorithm have been developed. The leap-frog algorithm [Hockney 1970] uses the following relationships  

---

Finite difference techniques are used to generate molecular dynamics trajectories with continuous potential models, which we will assume to be pairwise additive. The essential idea is that the integration is broken down into many small stages, each separated in time by a fixed time 6t. The total force on each particle in the configuration at a time t is calculated as the vector sum of its interactions with other particles. From the force we can determine the accelerations of the particles, which are then combined with the positions and velocities at a time t to calculate the positions and velocities at a time t + 6t. The force is assumed to be constant during the time step. The forces on the particles in their new positions are then determined, leading to new positions and velocities at time t - - 2St, and so on. 

There are many variants of the predictor-corrector theme of these, we will only mention the algorithm used by Rahman in the first molecular dynamics simulations with continuous potentials [Rahman 1964]. In this method, the first step is to predict new positions as follows ... 

One tool for working toward this objective is molecular mechanics. In this approach, the bonds in a molecule are treated as classical objects, with continuous interaction potentials (sometimes called force fields) that can be developed empirically or calculated by quantum theory. This is a powerful method that allows the application of predictive theory to much larger systems if sufficiently accurate and robust force fields can be developed. Predicting the structures of proteins and polymers is an important objective, but at present this often requires prohibitively large calculations. Molecular mechanics with classical interaction potentials has been the principal tool in the development of molecular models of polymer dynamics. The ability to model isolated polymer molecules (in dilute solution) is well developed, but fundamental molecular mechanics models of dense systems of entangled polymers remains an important goal. 

The ASEP/MD method, acronym for Averaged Solvent Electrostatic Potential from Molecular Dynamics, is a theoretical method addressed at the study of solvent effects that is half-way between continuum and quantum mechanics/molecular mechanics (QM/MM) methods. As in continuum or Langevin dipole methods, the solvent perturbation is introduced into the molecular Hamiltonian through a continuous distribution function, i.e. the method uses the mean field approximation (MFA). However, this distribution function is obtained from simulations, i.e., as in QM/MM methods, ASEP/MD combines quantum mechanics (QM) in the description of the solute with molecular dynamics (MD) calculations in the description of the solvent. 

For any dynamical simulation, a continuous representation of the PES is mandatory since the potential and the gradients are needed for arbitrary configurations. One can in fact perform ab initio molecular dynamics simulations in which the forces necessary to integrate the classical equations of motion are determined in each step by an electronic structure calculations. There have been few examples for such an approach [35-37], However, in spite of the fact that electronic structure calculations can nowadays be performed very efficiently, still there is a significant numerical effort associated with ab initio calculations. This effort is so large that in the ab initio dynamics simulations addressing molecular adsorption and desorption at surfaces the number of calculated trajectories has been well below 100, a number that is much too low to extract any reliable reaction probabilities. 

First, the current state of affairs is remarkably similar to that of the field of computational molecular dynamics 40 years ago. While the basic equations are known in principle (as we shall see), the large number of unknown parameters makes realistic simulations essentially impossible. The parameters in molecular dynamics represent the force field to which Newton s equation is applied the parameters in the CME are the rate constants. (Accepted sets of parameters for molecular dynamics are based on many years of continuous development and checking predictions with experimental measurements.) In current applications molecular dynamics is used to identify functional conformational states of macromolecules, i.e., free energy minima, from the entire ensemble of possible molecular structures. Similarly, one of the important goals of analyzing the CME is to identify functional states of areaction network from the entire ensemble of potential concentration states. These functional states are associated with the maxima in the steady state probability distribution function p(n i, no, , hn). In both the cases of molecular dynamics and the CME applied to non-trivial systems it is rarely feasible to enumerate all possible states to choose the most probable. Instead, simulations are used to intelligently and realistically sample the state space. 

---