Franz J. Vesely > CompPhys Tutorial > Selected Applications > Molecular Dynamics

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 Part III: Ch. 6

## 6.3 Molecular Dynamics Simulation

Fundamentally different methods for

(a) Hard bodies with impulsive collisons and
(b) Particles interacting via smooth potential forces.

Subsections

## 6.3.1 Hard Spheres / Hard disks

Set up the hard spheres/disks on a lattice; then assign random initial velocities to the $N$ particles. Adjust these velocities such that
• the total kinetic energy is consistent with some desired temperature: $E_{k}=3NkT/2$
• the total momentum (conserved in the simulation) equals zero.
Note that the adjustment of the temperature is only temporary; it will have to be repeated several times before thermal equilibrium is reached; even then, $T$ will continue to fluctuate.

Now calculate, for each pair of particles $(i,j)$ in the system, the time $t_{ij}$ it would take that pair to meet:

$t_{ij} = \frac{\textstyle -b - \sqrt{b^{2}-v^{2}(r^{2}-d^{2})}}{\textstyle v^{2}}$

where $d$ is the sphere diameter, $r$ is the distance between the centers of $i$ and $j$, and

$\begin{eqnarray} b & = & ( r_{j}- r_{i}) \cdot ( v_{j}- v_{i}) \\ v & = & \left| ( v_{j}- v_{i})\right| \end{eqnarray}$

Note that the above formula for $t_{ij}$ derives from a straightforward solution of the quadratic equation $r_{ij}^{2}(t)-d^{2}=0$. As discussed in the appendix, this solution may give rise to numerical errors. A more secure alternative is, with the same meaning for $b$ and $v$,

$q = - \left[ b +sgn(b) \sqrt{b^{2}-v^{2}(r^{2}-d^{2})} \right], \;\;\;\;t_{ij} = min \left\{ \frac{\textstyle q}{\textstyle v^{2}}, \frac{\textstyle r^{2}-d^{2}}{\textstyle q} \right\}$

(Since $b$ must be negative for a pair to meet in the future, we may restrict the calculation to the case $sgn(b)<0$.)

Set up two arrays that contain, for each particle $i$, the smallest positive collision time $t(i)=min(t_{ij})$ and the next collision partner $j(i)$. If a particle has no collision partner at positive times we simply set $j(i)$ and $t(i)=[\infty]$, i.e. the largest representable number.

This double loop over all $N(N-1)/2$ pairs need be performed only once, at the start of the simulation.

Now find the smallest element in the table of free flight times, calling it $t(i_{0})$. This is the time until the very next encounter between two particles in the system. The indices of those two particles are named $i_{0}$ and $j_{0}$.

During the time $t(i_{0})$ all particles perform a free flight, thus:
$r_{i}\longrightarrow r_{i}+ v_{i} \cdot t(i_{0})$

and all $t(i) \longrightarrow t(i)-t(i_{0})$.

Now an elastic collision between $i=i_{0}$ and $j=j_{0}$ occurs, resulting in the new velocities

$v_{i}'= v_{i} + \Delta v , \;\;\; v_{j}'= v_{j} - \Delta v$

where
$\Delta v =b \frac{\textstyle r_{ij}}{\textstyle d^{2}}$

Since $i_{0}$ and $j_{0}$ have new flight directions and speeds, all pairwise collision times $t_{ij}$ involving these two must be recalculated. This means that $2N-3$ pairs have to be scanned.

This completes the basic hard sphere/disc MD step. Now all $t_{i}$ are once more searched for the smallest element, etc.

Here is the procedure in succinct form:

 Molecular dynamics simulation of hard spheres: Immediately after a collision, for each particle $i$ in the system the time $t(i)$ to its next collision and the partner $j(i)$ at that collision is assumed to be known. Determine the smallest positive element $t(i_{0})$ among the $t(i)$, identify the corresponding particle $i_{0}$ and its collision partner $j_{0}\equiv j(i_{0})$. Let all particles follow their free flight paths for a period $\Delta t \equiv t(i_{0})$; subtract $\Delta t$ from each $t(i)$. Perform the elastic collision between $i_{0}$ and $j_{0}$; after the collision these spheres have the new velocities $v' = v \pm \Delta v , \;\;\;{\rm with} \;\; \Delta v = b \frac{\textstyle r_{ij}}{\textstyle d^{2}}$ Recalculate all times $t(i)$ that involve either $i_{0}$ or $j_{0}$, i.e. for $i=i_{0}$, $i=j(i_{0})$, $i=j_{0}$, and $i=j(j_{0})$. Go to (1). At low densities the large free path may create problems with the periodic boundary conditions, some particle suddenly appearing where it overlaps another. One therefore limits the time allowed for free flight such that for each particle and each coordinate $\alpha$ the free flight displacement fulfills $\Delta x_{\alpha} \equiv v_{\alpha} \Delta t \leq L/2-d$.
 Figure 6.5: Molecular dynamics of hard spheres

For a two-dimensional system of hard disks, write subroutines to a) set up an initial configuration (simplest, though not best: square lattice;) b) calculate $t(i)$ and $j(i)$; c) perform a pair collision. Combine these subroutines into an MD code. To avoid the difficulty mentioned at the end of the preceding figure, one might simply use reflecting boundary conditions, doing a "billiard dynamics" simulation.

## 6.3.2 Continuous Potentials

For continously varying pair potentials we have for a particle $i$ at any time $t$

$\ddot{ r}_{i}(t) = \frac{\textstyle 1}{\textstyle m} \sum \limits_{j\neq i} K_{ij}(t)\;\;\;\;{\rm with} \;\;\;\; K_{ij}\equiv -\nabla_{i}u(r_{ij})$

Considering the Lennard-Jones interaction, we find for the pair force

$K_{ij}= -24 \frac{\textstyle \epsilon}{\textstyle \sigma^{2}} \left[ 2 \left( \frac{\textstyle r_{ij}}{\textstyle \sigma}\right)^{-14}\right. -\left. \left(\frac{\textstyle r_{ij}}{\textstyle \sigma}\right)^{-8}\right] r_{ij}$

where $r_{ij} \equiv r_{j}- r_{i}$.

The above-mentioned nearest image convention (NIC) is used in the evaluation of the force acting on a particle.

Having determined this total force, the equation of motion for particle $i$ may be numerically integrated. A widely used technique is Verlet's algorithm

$r_{i}(t_{n+1})= 2 r_{i}(t_{n})- r_{i}(t_{n-1}) + b_{i}(t_{n})(\Delta t)^{2}$

(with $b_{i}\equiv \sum \limits_{j\neq i} K_{ij}/m$.)

Augment the subroutine module ENERGY such that it computes, for each Lennard-Jones particle $i$ in the system, the total force exerted on it by all other particles $j$: $K_{i} \equiv \sum \limits_{j \neq i} K_{ij}$, with $K_{ij}$ as given above; remember to apply the nearest image convention.

Write a subroutine MOVE to integrate the equations of motion by a suitable algorithm such as Verlet's. Having advanced each particle for one time step, do not forget to apply periodic boundary conditions to retain them all in the simulation box.

Write a main routine that puts the subroutines STARTCONF, ENERGY and MOVE to work. Test your first MD code by monitoring the mechanically conserved quantities.

Do a number of MD steps - say, $50-100$ - and average the quantity $| v^{*}|^{2}$ to estimate the actual temperature. To adjust the temperature to a desired value, scale all velocity components of all particles in a suitable way. Repeat this procedure up to 10 times. After $500 - 100$ steps the fluid will normally be well randomized in space, and the temperature will be steady - though fluctuating slightly.

## 6.3.3 Beyond Basic Molecular Dynamics

• orientation dependent potentials
• ionic or multipolar interactions
• polymer chains (geometrically constrained molecules)
• nonequilibrium dynamics (e.g. laminary flow); see [EVANS 86], [HOOVER 99]
• thermostatted dynamics (Nosé-Hoover)

Geometrical constraints / SHAKE method:
Internal geometrical constraints, e.g. rigid bond lengths (see table 6.2):
• Stiff harmonic bonds - uneconomical
• SHAKE method by Ryckaert et al. [RYCKAERT 77] - better

SHAKE: Consider the smallest non-trivial Kramers chain consisting of three atoms connected by massless rigid bonds of lengths $l_{1,2}$. Constraint equations:

$\sigma_{1}( r_{12})=\left| r_{12}\right|^{2}-l_{1}^{2} \;\;\;{\rm and}\;\;\; \sigma_{2}( r_{23})=\left| r_{23}\right|^{2}-l_{2}^{2}$

with $r_{12} \equiv r_{2}- r_{1}$ etc.

Lagrange constraint forces: keep the bond lengths constant. The constraint force on atom $1$ is parallel to $r_{12}$. Atom $2$ is subject to two constraint forces along $- r_{12}$ and $r_{23}$. Atom $3$ is kept in line by a force along $- r_{23}$:

$\begin{eqnarray} \ddot{ r}_{1}& =& b_{1}+\frac{\textstyle a_{1}}{\textstyle m_{1}} r_{12} \\ \ddot{ r}_{2}&=& b_{2}+\frac{\textstyle 1}{\textstyle m_{2}} \left[-a_{1} r_{12}+a_{2} r_{23}\right] \\ \ddot{ r}_{3}&=& b_{3}-\frac{\textstyle a_{2}}{\textstyle m_{3}} r_{23} \end{eqnarray}$

where $b_{1..3}$ are the physical accelerations due to Lennard-Jones or other pair potentials.

Procedure:
1. Let the positions $r_{i}^{n}$ be given at time $t_{n}$. Integrate the equations of motion for one time step with all $a_{k}$ set to zero, i. e. without considering the constraint forces; denote the resulting preliminary positions (at time $n+1$) as $r_{i}'$. These will not yet fulfill the constraint equations; instead, the values of $\sigma_{1}( r_{12}')$ and $\sigma_{2}( r_{23}')$ will have some nonzero values $\sigma_{1}'$, $\sigma_{2}'$.

2. Now we make the correction ansatz

$\begin{eqnarray} r_{1}^{n+1}&=& r_{1}'+\frac{a_{1}}{m_{1}} r_{12}^{n} \\ r_{2}^{n+1}&=& r_{2}'+\frac{1}{m_{2}} \left[-a_{1} r_{12}^{n}+a_{2} r_{23}^{n}\right]\\ r_{3}^{n+1}&=& r_{3}'-\frac{a_{2}}{m_{3}} r_{23}^{n} \end{eqnarray}$     (6.4-6.6)

with undetermined $a_{k}$, requiring that the corrected positions fulfill the constraint equations:

$\begin{eqnarray} \sigma_{1}' - 2 \frac{a_{1}}{\mu_{12}} \left( r_{12}^{n}\cdot r_{12}'\right) + 2 \frac{a_{2}}{m_{2}} \left( r_{23}^{n}\cdot r_{12}'\right) +\left[ \dots \right]^{2} &=& 0 \\ \sigma_{2}' + 2 \frac{a_{1}}{m_{2}} \left( r_{12}^{n}\cdot r_{23}'\right) - 2 \frac{a_{2}}{\mu_{23}} \left( r_{23}^{n}\cdot r_{23}'\right) +\left[ \dots \right]^{2} &=& 0 \end{eqnarray}$

with $1/\mu_{12} \equiv 1/m_{1}+1/m_{2}$; the terms $\left[ \dots \right]^{2}$ are quadratic in $a_{1,2}$.

Instead of solving these two quadratic equations for the unknowns $a_{1,2}$ we ignore, for the time being, the small quadratic terms. The remaining linear equations are solved iteratively, meaning that this system of linear equation is solved to arrive at an improved estimate for $a_{1,2}$ which is again inserted in 6.4-6.6 leading to a new set of linearized equations etc., until the absolute values of $a_{1,2}$ are negligible; generally, this will occur after a very few iterations.

Solving the linearized equations involves a matrix inversion. To avoid this we introduce one more simplification:
In passing through the chain from one end to the other we consider only one constraint per atom:

• First the bond $r_{12}$ is repaired by displacing $1$ and $2$.
• By repairing the next bond $r_{23}$ we disrupt the first bond again; this is accepted in view of further iterations.
• By going through the chain several times we reduce both the error introduced by neglecting the quadratic terms and the error due to considering only one constraint at a time.

In our case the procedure is:

$a_{1}=\frac{\textstyle \mu_{12}}{\textstyle 2} \frac{\textstyle \sigma_{1}'}{\textstyle r_{12}'\cdot r_{12}^{n}} \;\;\;\;\; a_{2}=\frac{\textstyle \mu_{23}}{\textstyle 2} \frac{\textstyle \sigma_{2}'}{\textstyle r_{23}'\cdot r_{23}^{n}}$

insert this in 6.4-6.6 and iterate until $a_{1,2}$ are negligible.
The technique is easily generalized to longer chains.

Molecules and robots:
Robot arms made up of several successive links and joints bear some resemblance to chain molecules. A standard problem of robotics, the inverse kinematic problem, may therefore be solved a la simulation.[KASTENMEIER 86]

The principle of the so-called constrained dynamics/stochastic optimization technique is as follows:
• Define a required "world trajectory" for the final element in the chain.
• If the robot is redundant, meaning that it has more degrees of freedom (links and joints) than necessary, the problem is underdetermined: different combinations of movements by the individual joints produce identical paths of the final member.
• This redundancy may be exploited to fulfill additional requirements, such as an overall minimum of angular accelerations (i. e. mechanical wear) in the joints, avoidance of obstacles, etc.
• The last element is now moved, one time step at a time, along its requested trajectory, and SHAKE is invoked to have the preceding joints follow.
• The additional requirements are combined into a cost function which is minimized, at each time step, using a stochastic search (simulated annealing or simple random search).

Thermostats / Nosé-Hoover method:
In a nonequilibrium process work must be invested which systematically heats up the system. In lab experiments a thermostat takes care of this; in simulations the problem of thermostating is non-trivial. The temperature of a molecular dynamics sample is not an input parameter to be manipulated at will; rather, it is a quantity to be "measured" in terms of an average of the kinetic energy of the particles,

$\langle E_{kin}\rangle \equiv \langle \sum \limits_{i} \frac{\textstyle mv_{i}^{2}}{\textstyle 2}\rangle = d \frac{\textstyle NkT}{\textstyle 2}$

($d \dots$ dimension). Suggestions as to how one can maintain a desired temperature in a dynamical simulation range from repeatedly rescaling all velocities ("brute force thermostat") to adding a suitable stochastic force acting on the molecules. Such recipes are unphysical and introduce an artificial trait of irreversibility and/or indeterminacy into the microscopic dynamics.

A interesting deterministic method of thermostating a simulation sample was introduced by Shuichi Nosé and William Hoover. Under very mild conditions the following augmented equations of motion will lead to a correct sampling of the canonical phase space at a given temperature $T_{0}$:

$\begin{eqnarray} \dot{ v_{i}}&=&\frac{\textstyle 1}{\textstyle m} K_{i}-\xi v_{i} \\ \dot{\xi} &=&\frac{\textstyle 2}{\textstyle Q}\left[ E_{kin}-3NkT_{0}/2 \right] \end{eqnarray}$

The coupling parameter $Q$ describes the inertia of the thermostat. The generalized viscosity $\xi (t)$ is temporally varying and may assume negative values as well.

For systems of many degrees of freedom (particles) this is sufficient to produce a pseudo-canonical sequence of states. For low-dimensional systems it may be necessary to use two NH thermostats in tandem [MARTYNA 92].

vesely 2006

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