Langevin dynamics

In physics, Langevin dynamics is an approach to the mathematical modeling of the dynamics of molecular systems, originally developed by the French physicist Paul Langevin. The approach is characterized by the use of simplified models while accounting for omitted degrees of freedom by the use of stochastic differential equations.

A molecular system in the real world is unlikely to be present in vacuum. Jostling of solvent or air molecules causes friction, and the occasional high velocity collision will perturb the system. Langevin dynamics attempts to extend molecular dynamics to allow for these effects. Also, Langevin dynamics allows controlling the temperature like a thermostat, thus approximating the canonical ensemble.

Langevin dynamics mimics the viscous aspect of a solvent. It does not fully model an implicit solvent; specifically, the model does not account for the electrostatic screening and also not for the hydrophobic effect.

For a system of N particles with masses M, with coordinates X=X(t) that constitute a time-dependent random variable, the resulting Langevin equation is [1]

M\ddot{X} = - \nabla U(X) - \gamma M \dot{X} %2B \sqrt{2 \gamma k_B T M} R(t)\,,

where U(X) is the particle interaction potential; \nabla is the gradient operator such that -\nabla U(X) is the force calculated from the particle interaction potentials; the dot is a time derivative such that \dot{X} is the velocity and \ddot{X} is the acceleration; T is the temperature, kB is Boltzmann's constant; and R(t) is a delta-correlated stationary Gaussian process with zero-mean, satisfying

\left\langle R(t) \right\rangle =0
\left\langle R(t)R(t') \right\rangle = \delta(t-t')

Here, \delta is the Dirac delta.

If the main objective is to control temperature, care should be exercised to use a small damping constant \gamma. As \gamma grows, it spans the inertial all the way to the diffusive (Brownian) regime. The Langevin dynamics limit of non-inertia is commonly described as Brownian dynamics.

The Langevin equation can be reformulated as a Fokker–Planck equation that governs the probability distribution of the random variable X.

See also

References

  1. ^ Schlick, Tamar (2002). Molecular Modeling and Simulation. Springer. pp. 435–438. ISBN 0-387-95404-X. 

External links