944 research outputs found
Rational Construction of Stochastic Numerical Methods for Molecular Sampling
In this article, we focus on the sampling of the configurational
Gibbs-Boltzmann distribution, that is, the calculation of averages of functions
of the position coordinates of a molecular -body system modelled at constant
temperature. We show how a formal series expansion of the invariant measure of
a Langevin dynamics numerical method can be obtained in a straightforward way
using the Baker-Campbell-Hausdorff lemma. We then compare Langevin dynamics
integrators in terms of their invariant distributions and demonstrate a
superconvergence property (4th order accuracy where only 2nd order would be
expected) of one method in the high friction limit; this method, moreover, can
be reduced to a simple modification of the Euler-Maruyama method for Brownian
dynamics involving a non-Markovian (coloured noise) random process. In the
Brownian dynamics case, 2nd order accuracy of the invariant density is
achieved. All methods considered are efficient for molecular applications
(requiring one force evaluation per timestep) and of a simple form. In fully
resolved (long run) molecular dynamics simulations, for our favoured method, we
observe up to two orders of magnitude improvement in configurational sampling
accuracy for given stepsize with no evident reduction in the size of the
largest usable timestep compared to common alternative methods
Least-biased correction of extended dynamical systems using observational data
We consider dynamical systems evolving near an equilibrium statistical state
where the interest is in modelling long term behavior that is consistent with
thermodynamic constraints. We adjust the distribution using an
entropy-optimizing formulation that can be computed on-the- fly, making
possible partial corrections using incomplete information, for example measured
data or data computed from a different model (or the same model at a different
scale). We employ a thermostatting technique to sample the target distribution
with the aim of capturing relavant statistical features while introducing mild
dynamical perturbation (thermostats). The method is tested for a point vortex
fluid model on the sphere, and we demonstrate both convergence of equilibrium
quantities and the ability of the formulation to balance stationary and
transient- regime errors.Comment: 27 page
Generating Generalized Distributions from Dynamical Simulation
We present a general molecular-dynamics simulation scheme, based on the Nose'
thermostat, for sampling according to arbitrary phase space distributions. We
formulate numerical methods based on both Nose'-Hoover and Nose'-Poincare'
thermostats for two specific classes of distributions; namely, those that are
functions of the system Hamiltonian and those for which position and momentum
are statistically independent. As an example, we propose a generalized variable
temperature distribution that designed to accelerate sampling in molecular
systems.Comment: 10 pages, 3 figure
A molecular-dynamics algorithm for mixed hard-core/continuous potentials
We present a new molecular-dynamics algorithm for integrating the equations
of motion for a system of particles interacting with mixed continuous/impulsive
forces. This method, which we call Impulsive Verlet, is constructed using
operator splitting techniques similar to those that have been used successfully
to generate a variety molecular-dynamics integrators. In numerical experiments,
the Impulsive Verlet method is shown to be superior to previous methods with
respect to stability and energy conservation in long simulations.Comment: 18 pages, 6 postscript figures, uses rotate.st
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