NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

/pdf/scalable-molecular-dynamics-with-namd-51o1z7rwp8.pdf

Scalable molecular dynamics with NAMD

NAMD is a parallel molecular dynamics code designed for high‐performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high‐end parallel platforms, as well as tens of processors on low‐cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1781–1802, 2005

/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

Scalable Molecular Dynamics with NAMD

We present a revision to the well known Stormer-Verlet algorithm for simulating second order differential equations. The revision addresses the inclusion of linear friction with associated stochastic noise, and we analytically demonstrate that the new algorithm correctly reproduces diffusive behavior of a particle in a flat potential. For a harmonic oscillator, our algorithm provides the exact Boltzmann distribution for any value of damping, frequency, and time step for both underdamped and over damped behavior within the usual the stability limit of the Verlet algorithm. Given the structure and simplicity of the method we conclude this approach can trivially be adapted for contemporary applications, including molecular dynamics with extensions such as molecular constraints.

pdf/a-simple-and-effective-verlet-type-algorithm-for-simulating-1lyd6ucpf4.pdf

A simple and effective Verlet-type algorithm for simulating Langevin dynamics

We present a revision to the well known Stormer–Verlet algorithm for simulating second order differential equations. The revision addresses the inclusion of linear friction with associated stochastic noise, and we analytically demonstrate that the new algorithm correctly reproduces diffusive behaviour of a particle in a flat potential. For a harmonic oscillator, our algorithm provides the exact Boltzmann distribution for any value of damping, frequency and time step for both underdamped and overdamped behaviour within the usual stability limit of the Verlet algorithm. Given the structure and simplicity of the method, we conclude that this approach can trivially be adapted for contemporary applications, including molecular dynamics with extensions such as molecular constraints.

The purpose of the present article is to compare different phase-space sampling methods, such as purely stochastic methods (Rejection method, Metropolized independence sampler, Importance Sampling), stochastically perturbed Molecular Dynamics methods (Hybrid Monte Carlo, Langevin Dynamics, Biased Random Walk), and purely deterministic methods (Nose-Hoover chains, Nose-Poincare and Recursive Multiple Thermostats (RMT) methods). After recalling some theoretical convergence properties for the various methods, we provide some new convergence results for the Hybrid Monte Carlo scheme, requiring weaker (and easier to check) conditions than previously known conditions. We then turn to the numerical efficiency of the sampling schemes for a benchmark model of linear alkane molecules. In particular, the numerical distributions that are generated are compared in a systematic way, on the basis of some quantitative convergence indicators.

/pdf/theoretical-and-numerical-comparison-of-some-sampling-12gg40ih0c.pdf

Theoretical and numerical comparison of some sampling methods for molecular dynamics

The notion of error in practical molecular and Langevin dynamics simulations of large biomolecules is far from understood because of the relatively large value of the timestep used, the short simulation length, and the low‐order methods employed. We begin to examine this issue with respect to equilibrium and dynamic time‐correlation functions by analyzing the behavior of selected implicit and explicit finite‐difference algorithms for the Langevin equation. We derive: local stability criteria for these integrators; analytical expressions for the averages of the potential, kinetic, and total energy; and various limiting cases (e.g., timestep and damping constant approaching zero), for a system of coupled harmonic oscillators. These results are then compared to the corresponding exact solutions for the continuous problem, and their implications to molecular dynamics simulations are discussed. New concepts of practical and theoretical importance are introduced: scheme‐dependent perturbative damping and perturbative frequency functions. Interesting differences in the asymptotic behavior among the algorithms become apparent through this analysis, and two symplectic algorithms, ‘‘LIM2’’ (implicit) and ‘‘BBK’’ (explicit), appear most promising on theoretical grounds. One result of theoretical interest is that for the Langevin/implicit‐Euler algorithm (‘‘LI’’) there exist timesteps for which there is neither numerical damping nor shift in frequency for a harmonic oscillator. However, this idea is not practical for more complex systems because these special timesteps can account only for one frequency of the system, and a large damping constant is required. We therefore devise a more practical, delay‐function approach to remove the artificial damping and frequency perturbation from LI. Indeed, a simple MD implementation for a system of coupled harmonic oscillators demonstrates very satisfactory results in comparison with the velocity‐Verlet scheme. We also define a probability measure to estimate individual trajectory error. This framework might be useful in practice for estimating rare events, such as barrier crossing. To illustrate, this concept is applied to a transition‐rate calculation, and transmission coefficients for the five schemes are derived.

The notion of error in langevin dynamics. i. linear analysis

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The notion of error in langevin dynamics. i. linear analysis