Time-propagation algorithms in molecular dynamics: Difference between revisions

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In molecular dynamics simulations, the positions <math>\mathbf{r}_{i}(t)</math> and velocities <math>\mathbf{v}_{i}(t)</math> are monitored as functions of time <math>t</math>. This time dependence is obtained by integrating Newton's equations of motion. To solve the equations of motion, various integration algorithms have been developed. The time dependence of a particle can be expressed in a Taylor expansion
In molecular dynamics simulations, the positions <math>\mathbf{r}_{i}(t)</math> and velocities <math>\mathbf{v}_{i}(t)</math> are monitored as functions of time <math>t</math>. This time dependence is obtained by integrating Newton's equations of motion. When integrating the equations of motions it is important to use symplectic algorithms which conserve phase space volume. To solve the equations of motion under symplectic conditions, various integration algorithms have been developed. The time dependence of a particle can be expressed in a Taylor expansion
::<math>
::<math>
\mathbf{r}_{i}(t+\Delta t) = \mathbf{r}_{i}(t) + \mathbf{v}_{i}(t)\Delta t + \frac{\mathbf{F}_{i}}{2m}(t)\Delta t^{2} + \frac{\partial^{3} \mathbf{r}_{i}(t)}{\partial t^{3}}\Delta t^{3} + \mathcal{O}(\Delta t^{4})
\mathbf{r}_{i}(t+\Delta t) = \mathbf{r}_{i}(t) + \mathbf{v}_{i}(t)\Delta t + \frac{\mathbf{F}_{i}}{2m}(t)\Delta t^{2} + \frac{\partial^{3} \mathbf{r}_{i}(t)}{\partial t^{3}}\Delta t^{3} + \mathcal{O}(\Delta t^{4})

Revision as of 19:14, 16 October 2024

In molecular dynamics simulations, the positions and velocities are monitored as functions of time . This time dependence is obtained by integrating Newton's equations of motion. When integrating the equations of motions it is important to use symplectic algorithms which conserve phase space volume. To solve the equations of motion under symplectic conditions, various integration algorithms have been developed. The time dependence of a particle can be expressed in a Taylor expansion

A backward propagation in time by a time step can be obtained in a similar way

Adding these two equation gives and rearrangement gives the Verlet algorithm

The Verlet algorithm can be rearranged to the Velocity-Verlet algorithm by inserting

Velocity-Verlet Integration scheme

The Velocity-Verlet algorithm can be decomposed into the following steps:

  1. compute forces from density functional theory or machine learning

From these equations it can be seen that the velocity and the position vectors are synchronous in time.

Leap-Frog Integration scheme

Another form of the Verlet algorithm can be written in the form of the Leap-Frog algorithm. The Leap-Frog algorithm consists of the following steps:

  1. compute forces from density functional theory or machine learning
MDALGO thermostat integration algorithm
0 Nose-Hoover Velocity-Verlet
1 Andersen Leap-Frog
2 Nose-Hoover Leap-Frog
3 Langevin Velocity-Verlet
4 NHC Leap-Frog
5 CSVR Leap-Frog