Langevin dynamics random force term generation algorithm

In summary, a user is seeking an algorithm for generating the stationary Gaussian distribution R(t) with zero mean and autocorrelation of A = 2 \gamma k_B T m. The R(t) is related to the simple Langevin equation and the user has been searching for code in a book by G. R. Luckhurst and C. A. Veracini and a paper by Chandler and Dellago from 1998.
  • #1
iibewegung
16
0
Hi,

Can anyone tell me an algorithm to generate the stationary Gaussian distribution R(t) with

[tex] \langle R(t) \rangle = 0 [/tex] (zero mean)
[tex] \langle R(t) R(t')^{T} \rangle = A \delta(t-t') [/tex], [tex]A = 2 \gamma k_B T m[/tex] (autocorrelation)

?

What I just wrote is from the Wikipedia article "Langevin dynamics"
and R(t) belongs to the simple Langevin equation
[tex]F(x) = m \ddot{x} = -\nabla U(x) - \gamma m \dot{x} + R(t)[/tex]
 
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  • #2
I have been looking for the same code. It is not exactly trivial. I found some code in the following book
"The Molecular Dynamics of Liquid Crystals,by G. R. Luckhurst, C. A. Veracini"
I have been looking for a DJVU copy of this book but haven't found one.
 
  • #3

1. What is Langevin dynamics and how does it relate to random force term generation?

Langevin dynamics is a computational technique used to simulate the motion and behavior of particles in a system. It takes into account both deterministic forces and random forces, which are generated using the random force term generation algorithm. This algorithm assigns random forces to each particle in the system, allowing for a more realistic simulation of the system's behavior.

2. How does the Langevin dynamics random force term generation algorithm work?

The algorithm works by generating a random number for each particle in the system, and then multiplying it by a predetermined factor to determine the magnitude of the random force. This force is then applied to the particle in a direction that is randomly chosen. This process is repeated for each particle in the system, resulting in a set of random forces that are applied to the particles.

3. What is the purpose of using random forces in Langevin dynamics simulations?

The use of random forces in Langevin dynamics simulations allows for a more realistic representation of the system being studied. It takes into account the random fluctuations and collisions that occur between particles in a real system, which can affect the overall behavior of the system.

4. How is the magnitude of the random force determined in the Langevin dynamics random force term generation algorithm?

The magnitude of the random force is determined by multiplying a random number by a factor known as the diffusion coefficient. This factor can be adjusted to control the strength of the random forces in the simulation, allowing for more control over the behavior of the system.

5. Are there any limitations or drawbacks to using the Langevin dynamics random force term generation algorithm?

One limitation of this algorithm is that it assumes the random forces acting on each particle are uncorrelated. This may not always be the case in certain systems, which can affect the accuracy of the simulation. Additionally, the use of this algorithm may result in longer simulation times compared to other techniques that do not incorporate random forces.

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