Molecular dynamics simulations: a set of lecture notes Simon W. de Leeuw and Rajiv K. Kalia† 

Computational Physics, Dept. of Applied Physics, TU Delft, Lorentzweg 1, 2628 CJ Delft, The Netherlands † Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA60201, USA

February 1, 2002

1

1

Introduction

In the previous sections we have shown, how thermodynamic behaviour and structural properties of classical systems can be calculated as ensemble averages with Monte Carlo techniques. Equilibrium properties can also be computed as time-averages. The time- and ensemble-average are the same when the system is ergodic. For an isolated system the appropriate ensemble is the microcanonical ensemble. Consider then a classical system of N particles in a volume V . The state of the system is then completely specified when the positions 1  q1  qN  and momenta  p 1  pN  are given. The microcanonical ensemble average of a dynamical quantity A  q 1  qN  p1  pN  is 



A

NV E

dq1 







dqN  dp1  dpN A  qN  pN   δ   qN  pN  dq1  dqN  dp1  dpN δ    qN  pN   E 



E

(1)

Here  is the Hamiltonian of the system and q N  pN  denotes the complete set of coordinates and momenta. The numerator is of course, apart from a multiplicative constant, the microcanonical partition function Ω  N  V  E  . We can also regard the classical system as a dynamical system. Given initial positions  q 1  0   qN  0  and initial momenta  p 1  0   pN  0  the classical equations of motion can be solved to yield the positions q t     1   qN t  and momenta p 1 t   pN t  at time t. As the system moves along its phase space  trajectory the value of the dynamical quantity A q 1  qN  p1  pN ;t  now changes with time. The energy E of the system remains constant. The time- average A¯ is: A¯

1 lim t ∞ t



t 0

d τ A  q1  qN ; p1  pN ; τ 

(2)

Note that we assume, that (for sufficiently long times t) A¯ is independent of the initial conditions. Usually we now write, that: 



A

NV E

(3)

Equation 3 is usually referred to as the ergodic hypothesis, which appears to be valid for systems in equilibrium2 . The ergodic hypothesis is by no means generally true though. Examples of systems for which the ergodic theorem is known to be invalid are meta-stable systems such as supercooled liquids and amorphous or vitreous materials. For example, we cannot speak of the equilibrium pressure of a glass. The properties of the glass are partly determined by the manner in which it has been prepared. For ergodic systems we can determine the equilibrium properties by time-averaging as in equation 2. In order to do so, we determine the phase space trajectory of the system and express various quantities of interest in terms of the positions and momenta of the system. For example, to compute the temperature of the system, we make use of the equipartition theorem, which states, that each quadratic term in the Hamiltonian contributes 12 kB T to the mean energy of the system. Thus we can define an instantaneous temperature   T t  in terms of the velocities v i of the particles through: ∑Ni 1 mi vi 2 kB d  N  1 

T  t 

(4)

Here d denotes the dimensionality of the system, i.e. d 2 for a 2-dimensional system (e.g. surface) and d 3 in the usual 3-dimensional world. The factor N  1 occurs, because the momentum in each direction is conserved, so that the total number of degrees of freedom is dN  d rather then dN. Then: 

1 t dt  T  t   (5)

t 0 The technique of molecular dynamics (MD) allows us to follow the trajectories of the constituent particles of a classical system in phase space as a function of time. This technique has the advantage that, besides equilibrium properties, transport properties and transient behaviour of the system can be investigated. T¯

1 we

shall use q to denote generalized coordinates and r to denote cartesian coordinates speaking we should also take into account in the microcanical average, that the linear and the angular momentum of an isolated system are conserved 2 Strictly

2

The MD technique was first applied in 1957 to a system of hard spheres by Alder [Alder 1957] , who used it to investigate the phase behaviour and transport properties in the hard sphere gas. Seven years later Rahman [Rahman 1964] published the first application of the MD technique to a system of atoms interacting through the more realistic Lennard-Jones potential. Since then the technique has been applied to a wide range of problems in condensed matter physics. Nowadays the technique is used to study realistic models of complex fluids, such as alkanes and alcohols, polymers and biomolecules, and solids, such as silicates, borate glasses and so on.

2

Molecular dynamics

The principle of the molecular dynamics technique is very straightforward. Given a potential function U  r1  rN  for a set of N interacting particles the classical equations of motion are solved numerically on a computer. Typically N  10 2  106 . The solution of these equations yields the trajectories p t    1   pN t   r1 t   rN t  of the particles in phase space. We shall first write down the classical equations of motion. We begin by considering the simplest possible case, namely N classical structureless particles with positions r 1  rN in a volume V interacting through a potential function, which is a sum of pairpotentials:

∑ u  ri j 

U  r1  rN 

(6)

i j

where ri j  ri  r j  is the separation between particles i and j. There is no external field. The force F i acting on particle i is then: Fi

∂U ∂ ri

!

∑ fi j

(7)

j" i

Here fi j is the force on particle i due to its interaction with particle j: fi j

!

∂ u  ri j 

#

du  ri j  ri j

∂ ri j

(8)

dri j ri j

The Newtonian equations of motion are then: mi r¨ i 



Fi

(9)



Given initial positions r i 0  and velocities r˙ i 0  for i 1  N the set of coupled ordinary second order differential equations 9 can be solved to yield the positions r i  t  and velocities r˙ i  t  at later times t. In a molecular dynamics simulation this is done numerically on the computer for a set of typically N  1000 particles. To mimic bulk material we have to introduce periodic boundary conditions for the same reasons as was done in a Monte Carlo simulation. Thus the particles are put in a cubic box, which on all sides is surrounded by exact replicas of itself. If a particle leaves the box on the right hand side its image enters the box on the left hand side and so on. For the sums in equations 6 and 7 the minimum image convention is assumed. For definiteness we shall illustrate the MD procedure with the Lennard-Jones system, for wich the interaction potential is:

φ  r  4ε

$&%

σ r'

12



%

σ r'

6(

(10)

This potential gives a reasonable representation of intermoleculair interactions in noble gases, such as Ar and Kr, and systems composed of almost spherical molecules, such as CH 4 and C  CH3  4 . To get an idea of the magnitude, for Ar values are ε ) k B 120K and σ 0 * 34nm The potential energy of the system is then: U

r1  rN + 4ε

N, 1 N

∑∑

i 1 j  i

3

-.0/

σ ri j 1

/

12



σ ri j 1

6 23

(11)

initialize: ri  r˙ i  do i = 1, no. of time-steps MD-loop call force (compute forces) call advance (advance one time-step δ t) collect data enddo MD-loop analyse results

step 1 step 2 step 3 step 4 step 5

Table 1: Schematic structure of an MD program The equations of motions then are: -. /

m¨ri

48ε ∑

i " j

/

12

σ ri j 1

N

σ ri j 1

1 2 

6 23

ri j ri2j

At this stage it is convenient to introduce reduced units. We measure length in units of σ , energy in units of of ε , mass in units of m and time in units of τ , with τ 2 mσ 2 ) ε . Typically τ is of the order of 1 psec. Thus:

r

4

E

4

t

4

r σ E ε t τ

Dimensionless units of temperature T and density ρ

T5

ρ5

(12)

N ) V are:

kB T ε ρσ 3

(13) (14)

In these units the energy of the system becomes: u  r1  rN + 4

N, 1 N

∑ ∑ % ri j ,

12

i 1 j  i



ri j ,

6

'

(15)

and the equations of motion reduce to: N

m¨ri

48 ∑ ri j , i " j 6

14



1 , r 2 ij

87

ri j

(16)

In this form the equations of motion have a simple structure. In addition we see that a theorem of corresponding states holds. Finally most quantities appearing in these equations are of order unity, which is advantageous from a computational point of view, since then computations are done with high accuracy. Hereafter we shall always use reduced units for the Lennard-Jones system. Schematically the structure of an MD program is then as shown in table 1. We shall discuss each of the steps in the table, using the Lennard-Jones systems an example.

2.1 initialization We begin reading in the parameters determining the state of the system, such as the density ρ and the temperature T . Then we assign positions and velocities to the particles. This can be done in several ways.

4

8

start from lattice positions This is the simplest way. For example, the Lennard-Jonesium crystallizes in a face-centred cubic lattice. As there are 4 atoms per unit cell the MD sample has N 4n 3 atoms. Hence the set of numbers 108, 256, 864 etc., which often occur in MD simulations of Lennard-Jones systems. The particles are then assigned random velocities, either uniformly distributed or drawn from a Gaussian distribution. The linear momentum P ∑ vi is set to zero. The velocities are then scaled to the required temperature. Thus we determine: Ti and multiply all velocities with a scale factor 8

∑Ni 1 9 i 2 dN  d



:

(17)

Tr ) Ti  , where Tr is the required temperature.

start from previously stored configuration If a number MD simulations already have been carried out we can use the final configuration of a previous run, stored on disc or tape. If necessary we can scale the velocities to the required temperature, using the same technique as described above.

An MD program usually has a set of options for different starting configurations.

2.2 force calculation Below we give Fortran code for the force calculation. The boxlength is denoted by boxl and rcsq r c2 , where rc is the cut-off distance for the potential, i.e. φ  r ; 0 for r < r c . We have also included the calculation of the virial Ψ Ψ

N, 1

N

∑ ∑

i 1 j  i= 1

ri j

d φ  ri j  dri j



(18)

which is required for the calculation of the pressure. This quantity is usually calculated along with the forces. For implementing periodic boundary condition, the >@?A>CB -function can be used. This is a Fortran defined function, which rounds of to the nearest integer. The force can evaluated in a loop over all particle pairs. This implies that the number of steps involves is proportional to N 2 , where N is the number of particles. In practice, however, we are often dealing with forces which vanish beyond a certain cut-off radius, and for these cases, the force can be evaluated much more efficiently, by including only those pairs whose separation is within the cut-off radius. A typical value for the cut-off radius is between 2.5 and 3 times the nearestneighbor distance. If this is done, the calculation takes only D  N  steps. There exist two techniques for considering only nearest neighbor pairs: the linked list method, originally introduced by Hockney [?], and neighbour tables, proposed by Verlet [Verlet 1967]. In the linked list technique the MD cell, with length l, is divided into M 3 smaller cells of size l ) M < rc . A particle in a given cell then only interacts with particles in the same or neighbouring cells. Thus we only need to search the cell a particle resides in and (half of) its neighbouring cells. This can be done very efficiently with a link list. In the link list technique we construct a header array, which points, for each cell, to the first particle in that cell. Then to each particle we assign a pointer, which points to the next particle in the same cell. The link list ends, when the pointer of a particle is zero. This is implemented below. We assume the particle coordinates lie between 0 and the boxlength boxl. N is the number of particles and the molecular dynamics cell has been divided into M 3 cells. dimension header(M,M,M), link(N) do k=1,M do l=1,M do m=1,M header(k,l,m) = 0 5

enddo enddo enddo do i 1  N link(i) = 0 enddo do i 1  N ixi = int(M*x(i)/boxl)+1 iyi = int(M*y(i)/boxl)+1 izi = int(M*z(i)/boxl)+1 link(i) = header(ixi,iyi,izi) header(ixi,iyi,izi) = i enddo A particle in a given cell now interacts only with particles in the same cell and its neighbours (26 in all). Because of Newton’s third law we only have to scan half of these each time. The algorithm above creates the list of particles within each cell. The force calculation can be performed using this list. This works as follows. FOR all cells with indices (ix, iy, iz) DO Fill the list xt, yt and zt with the particles of the central cell  icnt = 0; j = header(ix,iy,iz); WHILE (j E 0) DO j = link(j); icnt = icnt + 1; xt(icnt) = x(j); yt(icnt) = y(j); zt(icnt) = z(j); LocNum = icnt; END WHILE Now, LocNum is the number of particles in the central cell  FOR half of the neighbouring cells DO Find particles in the same way as central cell and append them to the list xt, yt, zt; END DO END DO Calculate Lennard-Jones forces between all particles in the central cell; Calculate Lennard-Jones forces between particles in central and neighbouring cells; A few remarks are in order. First, the minimal image convention must be respected in the calculation of the forces. Therefore, rather than taking dx = xt(j)–xt(i) as the distance between particles i and j along the x-axis, dx-BoxLength*NINT(dx/BoxLength) should be taken. Here BoxLength is the linear simulation box size, and NINT is a fortran function which returns the nearest integer of the argument. In the algorithm, a loop over ‘half of the neighbouring cells’ is performed. This avoids double counting. The cells over which the sum should run are ix+mx, iy+my, iz+mz, where the values mx, my and mz assume the following values: mx: my: mz:

0 0 1

0 1 0

1 0 0

0 1 1

1 0 1

1 1 0

0 1 –1

1 0 –1

1 –1 0

1 1 1

1 1 –1

1 –1 1

–1 1 1

It is convenient to store these triples in arrays. Another way to avoid including particles pairs with large separation in the force calculation is neighbour table. This is a table which, for each particle, stores the other particles which, at some time t, reside within a radius rt < rc of that particle. By going through the neighbour table of each particle we find all the other particles, with which the particle interacts. Depending on the size of r t and the diffusion rate of the particles 6

one needs to update the table every so often. The update is most efficiently carried out with the linked list method. We refer to the literature [Allen 1987] for a fuller discussion of the optimal choice of the algorithm and the optimal value of rt .

2.3 Advance particle positions A suitable algorithm is now used to integrate the equations of motion and advance the particle coordinates and momenta to their values at time-step t F δ t. Typically δ t  0 * 005. Several algorithms are known. The choice of the algorithm is influenced by considerations of efficiency and stability. For example, the force calculation is often the most time-consuming step in an MD simulation. This then makes it unlikely that Runge-Kutta algorithms, which require several force evaluations per time-step, are efficient. One of the most used algorithm is the socalled velocity Verlet algorithm [Swope 1982]. This algorithm is one off the class of Verlet algorithms [Verlet 1967] which are widely used in molecular dynamics simulations because of their relative simplicity and stability. The velocity Verlet algorithm is known to be reversible, i.e. is we reverse the velocities at a certain time the particles will retrace their trajectories. This automatically implies that the algorithm is absolutely stable. The positions and velocities are advanced according to:

ri  t F δ t G

ri  t 

vi  t F δ t G

vi  t 

δ t2  F t 2mi i  F  t F Fi  t F δ t  0* 5  δ t  i  mi 

F F

δ t  vi  t 

F

(19) (20)

In order to advance the velocities we require the force at time t F deltat. Thus one first advances the particle positions to time t F δ t and the velocities one half timestep. Then one computes the forces at t F δ t and finally completes the advancement of the velocities. The implementation of a single time step in the velocity Verlet scheme proceeds as follows: FOR all i DO ri ri F δ t  v i F δ t 2 F i ; vi vi F 0 * 5  δ t  F i ; END DO Calculate forces for current values of r i FOR all i DO vi vi F δ tFi END DO In this algorithm, it is assumed that the forces have been evaluated before starting the first step. The trick of this algorithm is that entering the forces at t and t F δ t into the array containing the velocities is done in two stages. At the second stage, the force at t is no longer needed, and can therefore be overwritten by the force at t F δ t. This possibility is not immediately recognised on inspection of the original algorithm. Another class of algorithms is based on predictor-corrector schemes has been proposed by Gear [Gear 1971]. These algorithms were used extensively in the early days of computer simulation and offer some advantages in special cases. However these algorithms are not time reversible and not absolutely stable. Nowadays these algorithms are rarely used.

2.4 Equilibration Averages of thermodynamic quantities obtained in an MD simulation do not depend on initial conditions if the simulation is carried out for a sufficiently long time. Nevertheless the starting conditions are usually far

7

from representative of the typical equilibrium conditions of the system. Therefore one usually divides the simulation into two parts. 8

8

equilibration The initial part of a simulation is usually referred to as equilibration. A suitable starting configuration is choosen and initial velocities are assigned. The MD-loop is carried out. If the system is far from equilibrium potential energy will be converted into kinetic energy or vice-versa. Thus the temperature changes. If required, the velocities are rescaled to the correct temperature. This is carried on until the system is stationary. Thus, although various quantities fluctuate, no systematic drift should be observed. The final configuration is stored. The various accumulators for structural and thermodynamic quantities are thrown away.

3

production The final positions and velocities (and, when the predictor-corrector technique is used, other timederivatives as well) are used to start the simulation. The MD-loop is repeated the required number of times. In an NVE- simulation the velocities are no longer rescaled. As the simulation proceeds values of thermodynamic, structural and dynamical quantities are accumulated for averaging afterwards. It should be noted that the production run often consists of a number of separate simulations, each starting where the previous simulation finished.

Thermodynamic and structural quantities

The calculation of equation of state data from an MD simulation is straightforward. Here we illustrate this for  the Lennard-Jones system. We denote time-averages by  . The following data are routinely calculated: 8

total energy E N, 1 N

E

4

∑ ∑ % ri j ,

12

i 1 j  i



ri j ,

6

'

F

1 N 2 vi 2 i∑  1

(21)

8

Obviously the total energy is constant (apart from small errors due to integration algorithm and rounding of), so that averaging is not required. temperature T

8



T5

1 3N  3



N

∑ v2i I

(22)

H i 1

pressure P The pressure P is calculated from the viral theorem:

PΩ NkB T

1 3 

N, 1 N

H



∑ ri j 

∂ φ  ri j 

i 1 j  i

∂ ri j

(23) I

For a Lennard-Jones potential this reduces to: PΩ NkB T

1F

8 N T5 

N, 1 N

JH

∑ ∑ % 2ri j ,

i 1 j  i

Note that in reduced units the unit of pressure is ε ) σ 3 .

8

12



ri j ,

6

'

I

(24)

8

specific heat The specific heat is calculated from fluctuations in the kinetic energy K. The wellknown fluctuation [Lebowitz 1967] formula reads: K

δ K2 L K



K

K

2 3N

2

6

1

3N 7 2CV

(25) K



K

where δ K 2 L K 2 L  K 2 . Note that, since the total energy is conserved, the fluctuations in potential energy V should equal the fluctuations in kinetic energy. Thus δ V 2 L δ K 2 L . Structural properties of a liquid are usually discussed in terms of the radial distribution function g  r  , defined through: 1 ρ2

g  r M

Ω N2

N

N

∑ ∑ δ  ri  δ  r 

rj

H i 1 j  " i N

I

N

∑∑δ r 

H i 1 j  " i

ri j 

(26) I

Essentially g  r  gives the ratio of the average number of particles at a distance r from a particle at the origin, compared to the average number in an ideal gas at the same density. The radial distribution function is an important physical quantity for several reasons. Firstly, it provides insight in the local structure of a liquid. Furthermore, ensemble-averages of pair functions can be expressed in terms of integrals over the radial distribution function. For example, for a system interacting through a pairwise additive potential φ  r  the energy can be written as: E

3 Nk T 2 B

2π N ρ F



∞ 0

r2 g  r  φ  r  dr

(27)

and the pressure as: 

2π N ρ ∞ 3  d φ  r  dr (28) r g r 3 dr 0 so that knowledge of g(r) is sufficient to compute equation of state data. Finally, the radial distribution function can be measured experimentally through X-ray or elastic neutron scattering experiments. The observed angular dependence of the intensity I  θ  is a measure of the static structure factor S  k  . PΩ NkB T

S  k M

F



1F ρ



1 F 4πρ



g  r  1 exp  ik  r  dr 

∞ 0

r2  g  r  1

sin  kr  dr kr

(29)

By inverting the Fouriertransform g  r  can be obtained. To calculate g  r  a histogram is made of pair separations in a simulation. This is implemented in the following code: NCO!P ]

df>

PRQTSVUXW0Q ? NCO!P ! Y P QTS\U ?[Z ^ O`_Xacb _ _Xbf_ _0gih N g ^ Bed ?A>@? ? d ?cj`B > dlk PoNcp0q[Ncp NcrqsNcr NctqsNct m j[n Z Z [email protected]@| m m ?[uwv jsnyxVzfB{x jsn Bc}edf> | m Y#P m m ? jsn B~v jsn  P m YC€iN m | ?A> ?A>CB~v ? dlz  |‚P  | }T?cj[Bv ?A> }T?ij[B~v ?&> Z„ƒ N df> ?[u NlNlO df> NlNCO

9

g(r) 1

r

Figure 1: Radial distribution function g(r) in dense liquid (solid line) and dilute gas (dashed line) of LennardJones molecules Suppose this has been done N h times. Then the average number at separation r



b F 0 * 5



δ r is:

hist  b  N … Nh

n  b 

The average number of atoms in the same interval in an ideal gas is: 4πρ 3

nid  b 

 

r F δ r

3

r3  

Hence: n  b nid  b 

g  r 

In figure 3 we show the typical behaviour of the radial distribution function g(r) for a dense fluid and a dilute gas. For small distances r the strong repulsive interactions between molecules lead to an excluded volume of radius  σ around a central molecule, so that g  r  0 for r † σ . Packing effects of spheres then lead to the typical oscillatory structure of g  r  observed in a dense liquid. For large distances there is no correlation between the positions of molecules and g  r  4 1 asymptotically. In a dilute gas only pairs of molecules will occupy the same region in space at any given time, so that the behaviour of g  r  is entirely determined by the pairpotential ϕ  r  : g  r

4



exp 6

ϕ  r kB T 

7

ρ 4

0

Dynamical properties

Besides equation of state and structural data an MD simulation can be used to obtain information on timedependent quantities and transport coefficients. It is shown in most standard texts on non-equilibrium phenomena that transport coefficients can be expressed in terms of socalled time correlation functions, which describe the decay of fluctuations of the system in equilibrium. We illustrate this for the case of self-diffusion of particles in a fluid. Consider a solute that is present is very low concentration in a fluid solvent. The solute can in fact be a few tagged solvent molecules. The concentration of the solute molecules is sufficiently low, so that their mutual interaction can be neglected. Let n  r  t  the macroscopic density of solute molecules at position r and time t. Thus n  r  t  ρ¯  r  t  , where the microscopic density ρ  r  t  is defined as

ρ  r  t 

N

∑δ % r

j 1

rj  t '

(30)

Here r j  t  is the position of the j th solute molecule at time t. The macroscopic density and the corresponding particle flux j  r  t  of the solute molecules satisfy an equation of continuity of the form: 10

∂  n r  t  F ∇  j  r  t  0 ∂t and a constitutive relation is provided by Fick’s law:

(31)

j  r  t  ! D∇n  r  t 

(32)

where D is the coefficient of self-diffusion. Fick’s law states, that the mass flow is proportional to the concentration gradient. Since the macroscopic thermodynamic mechanism for mass flow is a gradient in chemical potential, or, equivalently for dilute solutions, a concentration gradient, this seems a reasonable phenomenological relationship. Combining equation 31 with Fick’s law yields:

∂  (33) n r  t  D∇2 n  r  t  ∂t which is also referred to as Fick’s law. The solution of Fick’s law is readily obtained through Fouriertransformation: 

nk  t 

drn  r  t  exp  

ık  r 

(34)

with the result that nk  t  nk  0  exp  

Dk 2t 

(35)

Here nk  0  is the spatial Fourier transform of the solute density at time t 0 Let us now consider how the transportcoefficient D is related to microscopic dynamics. Consider the time-correlation function of the microscopic solute density G  r  t  , defined by: 

G  r  t 

ρ  r  t  ρ  0  0

(36)

This function describes the decay of density fluctuations in our system. However, since we are considering a very dilute solution, G  r  t  can also be interpreted as the probability P  r  t  of finding a particle at position r at time t, given there was a particle at the origin 0 at time t 0. The crucial step next is to assume, that fluctuations in microscopic quantities decay, in the mean, according to the laws that govern their macroscopic behaviour. This is in essence the content of Onsager’s regression hypothesis [Onsager 1931a, Onsager 1931b], which won him the Nobel prize in Chemistry. This is an intu ¨itively appealing hypothesis, that can be justified rigourously on the basis of the fluctuation-dissipation theorem. Hence the probability P  r  t  obeys

∂  P r  t  D∇2 P  r  t  ∂t

(37)

with solution Pk  t  Pk  0  exp  Since the particle is known to be at the origin at t



Dk 2t 

(38)

0 we have that

P

r  0 

δ

r  , whence

Pk  0  1 The Fouriertransform can now be inverted to yield:

K

K

1

P  r  t  P  r t 



4π Dt 

3 2

exp 6



r2 7 4Dt

(39)

Equation 39 describes the probability for the particle to travel a distance r in time t. Hence the mean square distance ∆r2  t  L  r  t  r  0 ‡ 2 L , usually referred to as the mean square displacement, is obtained as: K ∆r2  t  L





∞ 0

P  r t  r4 dr 6Dt

11

(40)

<∆r2(t)>

t

Figure 2: Mean square displacement of a tagged molecule as a function of time This result, which was first derived by Einstein in 1902, clarifies the physical meaning of the diffusion coefficient D. Note that, since Fick’s law is a macroscopic description, valid only for long times, the Einstein relation 40 is only valid after an initial transient time. In figure 4 the behaviour of the mean square displament is shown. To conclude the analysis, note that 

r  t  r  0 T

t

v  t   dt 

0

where v is the velocity of the tagged particle. Hence K ∆r2  t 

 L



t

t

dt 

0

0

K

d  v t    v  t    L

Differentiating both sides gives: K

d ∆r2  t  dt



L

2 v t



2



2

2



v

0



0

, t t

  

K

r  t  r  0 

r

t 

0 

r 

dt  K v  0   v  t   L

dt  v  0   v  t  

0



L

In deriving this result we have made use of two important properties of time correlation functions. Since the time-correlation function describes the average decay of fluctuations in equilibrium it must be stationary, i.e. independent of the origin of time.K Therefore: K A t  B t   L

A t F t  B t F t  

L

K functions must be even functions of time, so that: Furthermore time autocorrelation

A t  A t   L

C t 

Now clearly

t   C  t   



t 

K

lim

t ∞

d ∆r2  t  dt L



6D

Therefore: D

1 3



∞ 0

v  0   v  t  dt 12

(41)

Z(t)

1

t .5

Figure 3: Velocity autocorrelation function in dense liquid (solid line) and dilute gas (dashed line) of Lennard-Jones molecules Equation 41 is an example of a Green-Kubo relation, relating a transport coefficient to an integral over a time correlation function. For all transport coefficients such a relation exists. Finally consider the velocity relaxation time τ rel defined by

τrel

1 3





v  0  v  t  dt v2  0 



ˆ



mD kB T

This is the time it takes for the diffusional regime to dominate the motion and the time it takes for nonequilibrium velocity distributions to relax to the Gaussian equilibrium distribution. For time t ‰ τ rel the K K motion is inertial and we have that: ∆r2  t  L

v2 L t 2

kB Tt 2

In case the solute is a tagged solvent molecule the diffusion coefficient describes how a solvent molecule diffuses through the system in time. The velocity autocorrelation function Z(t) can then be averaged over all solvent molecules. In a dilute gas binary collisions are the only mechanism for changing the velocity. This can be regarded as a stochastic Poisson process, leading to an exponential decay of Z(t): kB T exp m

Z  t 

 6

t 7 τrel

In a dense liquid Z(t) shows a more complex behaviour. A molecule moves in a cage of its neighbours and collides after a short time. As a result the velocity autocorrelation function exhibits a negative region. For long times Z(t) exhibits the socalled hydrodynamic tail [Alder 1970a, Ernst 1971]: Z t ∝ t,

d 2

d being the dimensionality of the system. Note that this behaviour implies, through equation 41 that in 2 dimensional systems the diffusion coefficient does not exist. In figure 4 we show the typical behaviour of Z(t) for a system of Lennard-Jones molecules in a dilute gas or in a dense liquid. In an MD simulation Z(t) is calculated as a time-average: Z

t 

1 T



T 0

1 N  v t F τ  v  τ  dτ N i∑  1

(42)

which, in discretized form reads: Z  tm +

1 NNt

N

Nt

∑ ∑ vi  tnt 

i  1 nt  1



vi  tnt F

tm 

Here the sum over nt is over different time origins in the system and t m

13



mδ t.

For completeness we give below Green-Kubo relations for some other transport coefficients. The Green-Kubo relation for the viscosity is: K

η



1 ΩkB T



σxy  t  σxy  0  L dt

0

(43)

Here σαβ is an element of the stress-tensor: /

σαβ

 1 N ri jα ri jβ ∂ u ri j  2∑ ri2j ∂ ri j j" i

N





iα 9 iβ F

m9

i 1

(44) 1

Here α  β x  y z. Here we have assumed for simplicity, that the particles interact through a pairpotential u  r  . Note that the virial theorem gives immediately, that Š

σαβ ‹

PΩδαβ

where δαβ is the Kronecker δ The Green-Kubo relation for the thermal conductivity λ is:

λ

where the heat current j e is defined as:





dt je  t   je  0  dt

0

(45)

K

N

je



1 3ΩkB T

∑  εi 

N, 1 N

∑ ∑  v i  f i j  ri j

εi L  vi F

i 1

(46)

i 1 j  i

where εi is the energy of particle i:

εi

1 N  ϕ ri j  2∑ j" i

Note the factor 12 , ensuring that the total energy E

5



∑ni 1 εi is indeed the sum of single particle energies

Statistics

Both in Monte Carlo and molecular dynamics simulations the quantities of interest are usually obtained as averages over many configurations of the system. Since we are averaging a statistical error is invariably associated with these quantities and we have to determine the accuracy of our results. If the configurations, generated in a simulation, were independent standard statististal techniques could be applied to arrive at an estimate of the statistical error. Suppose our simulation covers a total of Œ time intervals (in case of Monte Carlo simulations read Monte  Carlo moves). We seek an estimate of the mean  value A of some quantity A  r N   pN   and the standard deviation. Let us denote by A¯  the estimate of A . Then clearly: 1

A¯ 

Œ



∑ Am

(47)

m 1

and if our simulation is done correctly and the ergodic hypothesis is valid, then A¯  Then an estimate for the variance of A¯ 





lim A  ∞

is:

14

(48)

K 9

Š

ar  A

A¯2 

M

1

1





Am ∑ An I





2 H m 1

Œ

L 2

A¯ 

‹  



n 1



∑ ∑  Am 

2 H m 1 n 1

Œ



1 Ž



1

A 



2 H m 1

Π

An 



A



Am I

∑ An I

H n 1

 I



∑ ∑ δ Am δ An I

(49)

2 H m 1 n 1

Œ



If successive configurations were uncorrelated δ A m δ AK n 9

1

ar  A



δmn , so that:

δ A2 L

Œ

(50)

which is the wellknown formula for the error estimate for a set of independent observations. In a Monte Carlo or MD simulation successive configurations are strongly correlated however. We rewrite equation 48 in the form:

9

ar 





1 \



2 H m 1

Œ

F

δ A2m I F

2



2 H m 1

Œ

∑ ∑



2 H

m 1 n m= 1

, 1



n 1

F

, m

∑ ∑

m 1 n 1

In the last term we recognize the time correlation function CK AA  t

∑ CAA  nδ t 



, ∞

H



1



δ A2m I

1 C 0 2 AA 

δ A2 L



δ Am δ An I

δ Am δ Am =

nI

nδ t  , and the sum

τAc δt

(51)

defines a correlation time τ Ac for the quantity A. Clearly τ Ac is a measure of the time correlations in the fluctuations of A persist. Hence if the duration of the simulation T  Ž Œ δ t  τAc we may write: K 9

ar  A

 

2

τAc T

δ A2 L

(52)

Compared with equation 49 we see that the root-mean-square (rms) error is proportional to



τAc T‘

. This

T‘ τAc

is hardly surprising, since the number of independent configurations is proportional to and the rms error is inversely proportional to the number of independent configurations. The results clearly show, that for accurate results one has to simulate for times much larger than the typical correlation times for the quantities of interest. In a number of cases, for example when we are studying hydrodynamic fluctuations or fluctuations, which drive a symmetry-breaking transition, the correponding modes have a large life-time, porportional to the square of their wave-length. The system size must be large compared to relevant wave-lengths. Hence to study such fluctuations very long simulations (simulation time ∝ L2 ) are required. The error estimate requires therefore the calculation of the correlation function C AA  t  . This is often a costly calculation. A particularly elegant way of calculating the error is through a blocking transformation, which systematically removes correlations, suggested by Petersen and Flyvbjerg in 1989 [Petersen 1989]. Suppose we wish to estimate the error in the quantity A from a simulations. Thus we have a set of observations Am  m 1  M of the quantity A, with estimates A¯ M for the mean and 9 ar  A M  for its variance. We now transform this set of observations into a new set by a blocking transformation  Am

1 A F A2m , 2 2m 15

1

(53)

From this new set of observations we can again estimate the mean and the variance. Clearly the tranformation leaves the estimate of the mean unaltered, i.e. A¯  M A¯ M . If successive configurations are uncorre2

lated we find for the variance: 9

ar  A

M 2

2 9 ar  AM 



(54)

By repeated application of this tranformation we gradually remove correlations between successive observations. We can check for this by plotting the quantity 2

σ A’

n

ˆ 9

+

ar  A ’ n 2n

ˆ 

versus the number of applications n of the blocking transformation, starting with n 0 (i.e. M Œ ). Initially, for small values of n, correlations will still be present and we generally obtain an underestimate ˆ of the standard deviation. After a few transformations, during which generally an increase in σ 2  A ’ n  , is observed the succcessive observations must become independent, so that the plot shows a horizontal line. For large values of n we only have a few observations, so that the statistics are not reliable. If there is no horizontal region the simulation hasn’t lasted long enough and it is not possible to obtain a reliable error estimate. Finally we mention, that a similar transformation can be used to obtain error estimates in time correlation functions. For details we refer to the literature [Petersen 1989].

6

Long range interactions

6.1 Lattice sums When particles interact through a potential, such as the Lennard-Jones potential, we can cut-off the interaction at distances rc < 12 l. Many interesting cases exist, where this is no longer possible. The important case of electrostatic interactions falls amongs this category. Thus if we want to simulate systems, containing ions and polar molecules, it is no longer permissible to cut off the potential and only take minimum image interactions. In such cases we need to include other image beside the nearest image if we wish to evaluate the potential energy of the system. We illustrate the procedure for the case of ionic interactions. Other cases can be dealt with in a similar way. Consider then a system of N ions of charge z i and positions ri in a cubic box of size l. As before we impose periodic boundary conditions. Furthermore we insist, that the system is electrically neutral, so that: N

∑ zi

0

(55)

i 1

We compute the electrostatic potential ϕ  r  at some point r in the central cell:

ϕ  r 

N

∑∑

n i 1  r 

zi ri F nl 

(56)

Here the sum over n  n x  ny  nz  is over all translation vectors of the simple cubic lattice. Thus the n α are all possible integers. We now realize why we need to insist on charge neutrality. If the sytem is not charge neutral the sum 56 diverges. Even for a charge neutral system this sum is only conditionally convergent. In other words, we have to give meaning to the sum by stating the order, in which the various terms in the summation are added. This can be done by considering a finite large periodic system, for example a system, for which  n  †”“ , so that we consider only those images inside a sphere of radius R “ l. Afterwards we can let “ become arbitrary large. Thus we are considering a very large periodic array of identical systems, packed inside a sphere of radius R. An alternative is to introduce a socalled convergence factor, which makes the series 56 absolutely convergent (in which case we may alter the order of terms any way we like) and take the appropriate limit, so that the result reduces to equation 56. We shall follow this latter strategy. Thus we consider the series:

16

zi exp   sn2 l 2  1  r  ri F nl 

N

∑∑

ϕ  r; s ;

n i

(57)

This series is now considered as a function of the convergence factor s. For all s E 0 this is an absolutely convergent series, so that we can manipulate the various terms accordingly. In the limit s 4 0 the series reduces to the required electrostatic lattice sum. Note that the convergence factor treats each term with the same  n  in the same manner, so that effectively we are adding spherical shells, corresponding the case discussed above, namely a spherical system containing a large number of identical images of the central cell. A rigourous justification of this procedure is given in reference [deLeeuw 1980]. To manipulate this series into a form amenable to computation we need the following mathematical formulae. r,

2a



1 r



1 Γ  a  1 • π



dt 0 ∞ 0

•

exp   r 2t  t a, 1

dt exp  t 

(58)

r2t 

(59)

The first of these equations merely utilizes the definition of the Γ function, the second is an application of the first for the case a 12 . The second identity we require is: ∞



exp 

k T, ∞

 

k F x  2t 

π t 







exp

k T, ∞

6



π 2 k2 7 exp  2π ıkx  t

(60)

This result is easily derived by noting that the left hand side of equation 60 is a periodic function with a period of unity, so that it can be expanded as a Fourier series: ∞



exp 

k @, ∞

 



k F x 2 t 





k T, ∞

f  k  exp  2π ıkx 

(61)

with 

f  k –



1

dx ∞



, ∞

m @, ∞

0







dx exp 

π exp t 6





exp  



m F x 2t  exp 

x2t  exp 



2π ıkx 

2π ıkx  

(62)

π 2 k2 7 t

which proves equation 60. This result is a form of Jacobi’s imaginary transformation for Θ-functions, as discussed in for example Whittaker and Watson [Whittaker 1926]. Equation 60 is easily generalized to 3 dimensions to read:

∑ exp   n



n F r 2 t 

%

π t'

3 2

∑ exp k

6



π 2 k2 7 exp  2π ık  r t

(63)

The sums over n  n x  ny  nz  and k  kx  ky  kz  is over all possible sets of integers, i.e. n α  kα

  2   1  0  1  2  for α x  y z. We are now in a position to derive a rapidly convergent expansion for the lattice sum 55. Using the first identity, we can write:

17

zi exp   sn2 l 2  ∑ ∑ r r F nl n i 1   i  N

N



2 π

∑ ∑ zi • n i 1



dt exp % π •

0

 

r  ri F nl  2t exp 

sn2  

'



(64)

The integral is split into two parts: 







0



α2



F

0

α2

where α is a disposable parameter having dimensions of length , 1 . Consider first the upper integral, which can be carried out immediately: N

ϕII  r; s –

∑∑



∑∑

n i 1 N

zi exp  •

zi exp 



π



sn2 



dt exp % t •

α2

 

r  ri F nl  2t '

sn2  er f c  α  r  ri F nl    r  ri F nl  

n i 1

Here er f c  x  denotes the complementary error function: er f c  x 



2 π •



exp 

x

u2  du 

For large values of the argument the error function behaves as: er f c  x 

exp   x2  • x π 

Therefore the series for the upper part is absolutely convergent for all s obtain:

<

0 and we may set s

zi er f c  α  r  ri F nl    r  ri F nl  1



0 to

N

∑∑

ϕII  r; 0 

n i

(65)

The lower part of the integrand is written as: N



ϕI  r; s \

•

i 1

…

2zi π

exp 6





α2

•



0

dt t

r  ri 

/

ts

2

∑ exp 7

tF s

n



r  ri  s l

t

nF 

tF

6

2

7



s F t  l2 1

We now use the identity 60, which in our case reads: /

∑ exp n

1 l3 6

π

3 2

7

sF t

where the sum over k

∑ exp k



6





t

nF

tF

6

π 2 k2 l2  s F t  7

…



r  ri  s l

exp 2π ık  6

6

7

2



s F t  l2

r  ri 7 t 7 l sF t

k x  ky  kz  is over all possible sets of integers k α , α 18

1



x  y z. Then we obtain:

π zi 3 1 l

N

∑∑

ϕI  r; s \

k i

…

exp

α2

dt t :

0

π 2 k2

 6



7

l2  s F t 

s F t

exp

3

 

6

exp 2π ık  …

6

2

r  ri 

ts 7 tF s

r  ri 7 t 7 l sF t 6

We now consider two cases. First consider the terms with k — 0. In that case the integral is convergent even when s 0 to give: 

α2

dt t :

0

exp

3

s F t



6



7

6

α2

dt exp t2

0

π 2 k2 7 l 2t 

6

exp

tF s

exp 2π ık  …



ts

2

r  ri 

π 2 k2 s F t

 6

l2 

exp 2π ık  …

6

… 

r  ri 7 t 7 l sF t 6

7

s˜ 0

6

r  ri 7™7 l

which through the substitution u 1 ) t can be integrated immediately to give: 

α2 0

dt exp t2

π 2 k2 7 l 2t 

6

exp 2π ık  …

6

π 2 k2 α 2 l2 ' 2 2 π k ) l2

exp %



r  ri 7™7 l 6

exp 2π ık  …

6



6

r  ri 7š7 l

Finally, for the term with k 0 we have a divergence as s 0, so that a more careful analysis is required. Summing over all charges we obtain:

πz ∑ l3 i i 1 N

πz ∑ l3 i i 1 N





α2

α2

s F t

t

3



dt :

0

dt t :

0

s F t



p 0

 

6 



3

exp

p

1 p!

2

ts

p

7

tF s 6

ts 7 tF s

r  ri 





r  ri 

2p

Clearly the first term in the expansion of the exponent does not depend on the particle positions r i , so that this term vanishes upon summing over i because of charge neutrality. For p 1 we have 

α2

s 0

All other terms are

D

 s p, 1 

dt  sF t 

2





t sF t

›

2

α sœ α 2



2 3

x2 dx s ˜ 0

0

(66)

and vanish if s 0. Collecting all terms we obtain: zi er f c  α  r  ri F nl    r  ri F nl  1

N

∑∑

ϕ  r M

n i

π 2 k2

F

exp %  α 2 l 2 1 ' 2 ) l2 π l 3 k∑ k " 0 

2π l3

N

∑ zi  r 

i 1

ri 

2

N

…

∑ zi exp

i 1

6

2π ık  6

r  ri 7™7 l (67)

This is the result for the potential in a large spherical array of periodic copies of our molecular dynamics cell. The transformation 60 was first used by Ewald [Ewald 1921] to evaluate lattice sums in crystalline 19

solids. The electrostatic energy of our MD cell is now obtained as: Uel

1 N zi ϕ  ri  2 i∑  1

(68)

We should be careful however, as ϕ  r i  also includes a contribution due to ion i itself. This contribution should be subtracted out. Using: er f c  α r  0 r

lim

r

1 r 

!

•

2α π

we finally obtain: N N z z er f c  α r F nl 1 i j  ij  ∑∑ 2∑ r nl F n i  1 j  1  ij 

Uel

• 

2π 3l 3 F

Here the denotes that, when n  the charge density:

α π



N

/



i 1

π 2κ 2

exp %  α 2 1 2π l 3 κ∑ κ2 " 0

z2i F

'

κ



2

2

N

∑ zi ri

i 1

(69) 1

0, the term i

j should be omitted. ž

κ is the Fourier transform of

N

ž

κ

∑ zi exp  2π ıκ  ri 

(70)

i 1

with κ  kx  ky  kz  ) l. In writing the last term in equation 69 as a quadratic form we have again used the charge neutrality condition. Note that N

∑ zi ri

M

(71)

i 1

is the dipole moment of the MD cell. In a crystalline solid this term is usually not present, otherwise we would be dealing with a ferro-electric material. In a molecular dynamics simulation natural fluctuations arise, which lead to a dipole moment per unit cell. Because of the enforced periodicity this then gives rise to a uniformly polarized medium with polarization P M ) Ω, which in turn leads to a depolarizing field with corresponding energy 2π M P (72) 3 In careless applications of the Ewaldsum this term is usually left out. We may extend our model even and consider our periodic system embedded in a dielectric medium of dielectric constant ε  , as sketched in fig. 1. Region II is the dielectric with dielectric constant ε  . Region I is the sphere, of radius Ÿ , containing the periodic array of MD cells, which, from the point of view of electrostatics, may be treated as a uniformly polarized medium with dielectric constant ε . Region II is much larger then region I, so that we may consider it as infinite. Now, if, as a result of some thermal fluctuation, region I is uniformly polarized, region II will also be polarized. This gives rise to a reaction field E R acting on the particles in region I. Consider a uniform external field E  0  0  E z  in the z-direction being applied to our system. Hence far away from the sphere the potential has the form φ  r T   E z r cos θ . Using standard electrostatics we can calculate the potential anywhere. The symmetry requires the potential to have the form: Udep

φα  r 



Aα cos θ r2 20

F

Bα r cos θ

(73)

at r

with α I  II. Far away only the external field must be felt, so that B II

Ÿ require continuity of the potential and ε … ∂ φ ) ∂ r, leading to: AI Ÿ

2 

BI

AI 3

F

BI

Ÿ

AII

F

3

3 

Ez

2ε 

AII

Ÿ 

Ÿ

¡

Ez . The boundary conditions

ε  Ez

3 

The term containing A I corresponds to the field arising from uniformly polarized medium, so that AI

4π Ÿ 3

3

4π Ÿ 3

 P &

3  M



with M given by equation 71. For the calculation of the reaction field we may set E z BI . The reaction field is then E R  0  0   BI  . This yields:

0 and solve for

8π ε   1 M (74) 3 2ε  F 1 Ω The reaction field acts on the ions in the MD-cell, with energy U R M  ER ) 2. The factor of 1 ) 2 arises, since this term is really a free energy: it is the work required to charge the ions to their final value z i . Effectively the reaction field reduces the depolarizing field appearing in equation 72. When the two terms are combined we obtain: ER

#

Udep F UR



2π 2ε 

1 Ω F

M2

(75)

and the Ewald sum becomes: N N  z z er f c  α r F nl 1 i j  ij  ∑∑ F 2∑ r nl  ij  n i 1 j  1

Uel

•

α π

 F



N



i 1

π 2κ 2

exp %  α 2 1 2π l 3 κ∑ κ2 " 0

z2i F

2π 2ε  F

1 Ω

'



κ

2

M2

(76)

Thus we see that the electrostatic energy of the system depends on the shape of the sample as well as on its surroundings. This is to be expected, as the potential inside region I is the solution of the Poisson equation, which depends on the boundary conditions (shape, surroundings). It can be shown [de Leeuw 1981, Caillol 1994] that this term has no effect on the thermodynamic properties of the system except for the fluctuations in dipole moment. In fact, the last term behaves as a weakly coupled oscillator with average energy kB T ) 2. Note that, when ε  4 ∞, corresponding to a conducting medium outside region I, the last term vanishes altogether. In simulations one usually leaves this term out. This case is usually referred to as tinfoil boundary conditions. In that case no macroscopic field can be present in the system (Faraday’s cage). It is now straightforward to calculate the force acting on an ion. Differentiation of equation 76 yields: Fel ¢ i

∑ ∑ j"

N



n



z z G  ri j F i i j

nl  

π2κ2





exp %  α 2 4 κ ∑ Ω κ " 0 κ2 



2ε  F

1 Ω

zi M

'

ℑ  exp  

2π ıκ  r i ž

κ

(77)

Here ℑ denotes the imaginary part and 21

II

ε 

I

£E

Figure 4: Geometry for the calculation of the reaction field E R

2› α x exp   π

G  x 

α 2 x2  F

er f c  α x 

x3 Equation 77 can be used as starting point for the electrostatic energy of systems containing dipoles, quadrupoles and higher moments. For example, the electrostatic energy U dd of a periodic system containing N dipoles µ i at positions ri is obtained by making the replacement qi

4 

µ i  ∇i

The result [deLeeuw 1980, Allen 1987] is: N N  1  µ µ G  r F nl ∑ ∑  ij  i j 2 n i  1 j∑  1

Udd

N 

N

∑∑ ∑ 



n i 1 j  1

2α 3 • 3 π 

F 

N



i 1

µ i   ri j F nl   µ j   ri j F nl  H   ri j F nl   π2κ2

µi2 F

2π 2ε  F

1 Ω

exp %  α 2 4π ∑ 3 l κ " 0 κ2

'

 Mκ 

2

M2

(78)

Here N



∑ κ  µ i exp  2π ıκ  ri 

(79)

i 1

is the Fourier component, of wave-vector κ , of the polarization density and H

x 

2› α x  π

3 F 2α 2 x2  exp  

α 2 x2  F

3er f c  α x 

x5

G

and x  has been defined earlier. This is the Ewald-Kornfeld sum. The technique and implementation, on sequential as well as parallel architectures, of lattice sums of the type described above, is discussed extensively in the article by Perram et al [Perram 1989]. 22

6.2 Fluctuation formulae for the dielectric constant Consider now the calculation of the dielectric constant of a polarizable medium. We consider again the situation depicted in figure 6.1. We may now set A I 0 and compute the polarization of a uniform isotropic medium of dielectric constant ε under the action of the field E. The polarization in region I is:

ε

P

1 



EI

ε

 1 3ε  E 4π 2ε  F ε

(80)



Now, in our simulated system, the polarization is P M &¤¥) Ω, where M is the dipole moment of our simulation cell. The field ¦ here is the cavity field in region I, which is obtained from equation 80 by setting ε 1: ¦

3ε  E 2ε  F 1

A statistical calculation gives: 



M ¤



dΓM exp   β  dΓ exp   β   

0F 0F

M M ¦

¦  

where dΓ denotes integration over all of phase space. Linearizing the expression w.r.t.

¦

yields:

K 

M ¤



1 β K δ M2 L 0 ¦ 3 3ε  1 E β M2 L 0 3 2ε  F 1





where  0 denotes an equilibrium average. We have used M Combining these equations we obtain: 

ε 

1  2ε  F 1  3  ε F 2ε 

where we have defined



0

4πρβ  g ε  9

0.

(81)

K

M2 L N

g  ε  

Clearly the fluctuations in dipole moment depend on ε  . They depend on ε  in such a way that the dielectric constant ε is independent of the surroundings. A rigourous proof of this has been given by de Leeuw et al. [deLeeuw 1980].

7

Extended ensembles

7.1 Lagrange and Hamilton’s equations of motion Often the particles under study are not simple pointlike particles, such as Lennard-Jones atoms, but have internal structure and internal degrees of freedom. In that case the equations of motion become more complex. In this section we give the general formalism, due to Lagrange, for deriving equations of motion in more complex systems. This formalism will be used to derive equations of motion in socalled extended ensembles. The equations are derived from a postulated Lagrangian using the formalism described below. In the next section we shall use the Lagrangian and Hamiltonian formalism to derive a general set of equations of motion for semi-flexible molecules with a set of holonomic constraints. Consider a system described by a set of coordinates q 1  qN and velocities q˙ 1  q˙ N . The potential energy U  q1  qN  is usually a function of the coordinates only. The kinetic energy K may depend on the positional coordinates as well as the velocities (for example the rotational kinetic energy):

23

K K  q˙ 1  q˙ N  q1  qN  . In general K is a homogeneous quadratic function 3 of the velocities b ˙f qi . The Lagrangian § of the system is simply the difference of kinetic and potential energy: §



q1  qN  q˙ 1  q˙ N  K  q˙ 1  q˙ N  q1  qN  U  q1  qN 

(82)

Note that § is a function of 2dN (d is the dimensionality) independent variables, namely dN generalized coordinates and dN generalized velocities. The Lagrangian equations of motion are: d ∂§ dt ∂ q˙ i

∂§ ∂ qi 

0

(83)

These form a set of dN coupled ordinary second order differential equations for the coordinates q i , which can be solved if the initial positions q i  0  and velocities q˙ i  0  are known. It is easily shown that, for simple pointlike particles, these equations reduce to Newton’s equations of motion 9. In general these equations may become more complex however. For example, the motion of a particle in a plane may be described by polar coordinates r θ . The Lagrangian is: §



1 2 1 2 ˙2 m˙r F mr θ  U  r θ  2 2

r θ  r˙  θ˙ 

Using equation 83 we find

m¨r

mrθ˙ 2 

mr2 θ¨ F 2mrr˙θ˙



∂U ∂θ

∂U ∂r

It is easily seen, when r˙ 0, these reduce to the wellknown form for motion on a circle. The momentum p i conjugate to coordinate q i is defined as pi

∂§ ∂ q˙ i

(84)

In Hamilton’s formulation the conjugate momenta p i are treated on equal footing with the coordinates qi . The Hamiltonian  is defined through a Legendre transformation of the Lagrangian § as: N



∑ pi  q˙ i 



§

(85)

i 1

Using equation 84 we now replace all the velocities in favor of the conjugate momenta. Since the kinetic energy is a homogeneous quadratic function of the velocities q˙ i we can apply Euler’s theorem for homogeneous functions 4 of the second degree to write: 



q1  qN  p1  pN  K  p1  pN  q1  qN  F

U  q1  qN 

(86)

where the kinetic energy K is a homogeneous quadratic function of the conjugate momenta p i . For systems, in which Hamilton’s function does not depend on time explicitly,  is simply the total energy expressed as a function of coordinates and momenta, so that  is a constant of the motion. The Hamiltonian equations of motion are: q˙ i 3A



∂ ∂ pi

homogeneous function of degree n is one for which f ¨ λ x ©fª λn f ¨ x © theorem for homogeneous functions of degree n states that

4 Euler’s

m

∂f

∑ xi ∂ x

i« 1

i

ª n f ¨ x1 ®¬ ­¯­®­°¬ xm ©

24

(87)

p˙ i

∂ ∂ qi

#

(88)

These form a set of 2dN first order differential equations for the momenta and coordinates in the system, which are generally referred to as Hamiltonian equations of motion. Given initial conditions  q1  0   qN  0  ; p1  0   pN  0   these can be solved to yield the phase space trajectories. For example, for a system of pointmasses interacting through a potential function U  r 1  rN  the hamiltonian is: 

1 N 2 pi F U  r1  rN  2m i∑  1

leading to the following equations of motion:



p˙ i

pi m

r˙ i



∂U ∂ ri

which is equivalent to the Newtons equation 9. A second example is the motion of a particle in a plane, for which the Lagrangian is given in equation 7.1. The conjugate momenta p r and pθ are: pr



m˙r mr2 θ˙

leading to a Hamiltonian:



pr r˙ F pθ θ˙

p2r 2m

F

 

pθ2 2mr2 F

1 2 1 2 ˙2 m˙r F mr θ  U  r θ  2 2 U  r θ 

(89)

The Hamiltonian equations of motion are then:

∂H pr ∂ pr m ∂H ∂U ∂ r # ∂ r

p˙r

!

pθ ∂H ∂ pθ mr2 ∂H ∂U p˙θ ! ∂ θ # ∂ θ

θ˙



 



(90) (91)

Note, that if the particle moves in a central force field U is independent of θ , so that p˙ θ 0. This is the law of conservation of angular momentum, which follows in a natural way from the Hamiltonian formalism. The Lagrangian and Hamiltonian formalism’s provide powerful generalizations of the standard Newtonian approach to mechanics. They form the starting point in our derivation of the equations of motion in generalized ensembles. The general technique is to postulate a Lagrangian or Hamilonian, which couples to external parameters, such as pressure or temperature, and use the formalism described above to derive a suitable set of equations of motion for the system, which also involve these external parameters. Finally we remark, that, because of the transformation properties of the generalized coordinates and momenta, the Hamiltonian formalism is particularly useful in Statistical Physics.

7.2 Constant pressure simulations Conider a system of N particles with positions r i  i 1  N in some volume Ω. For simplicity we only consider pointmasses. As usual we impose periodic boundary conditions. The Lagrangian of the system is:

25

§

1 N m˙ri2  U  r1  rN  2 i∑  1

1

We now scale all length’s by a factor Ω 3 , so that: 1

ri

r˙ i

Ω 3 ρi

(92)

1 3

(93)

Ω ρ˙ i

Then in terms of scaled coordinates we have: 2

§



Ω3 2

ρ 1  ρ N  ρ˙ 1  ρ˙ N 

N

∑ mρ˙ i2 

1

1

U  Ω 3 ρ 1  Ω 3 ρ N 

i 1

Now imagine that the system can expand or contract in such a way, that the internal pressure is P int , on average, balanced by an externally applied pressure P. As the volume Ω changes through expansion or contraction by an amount ∆Ω work is done on the system equal to  P∆Ω. This can be incorporated in the Lagrangian § in a straightforward manner. The next step is to consider the volume Ω as a dynamical variable, which, for given externally applied pressure P, changes in response to fluctuations in the internal pressure. Since we wish to derive an equation of motion for the volume Ω we associate a kinetic energy 1 ˙2 2 W Ω with the change in volume. Thus we postulate a Lagrangian: 2

§



Ω3 2

˙ ρ 1  ρ N  Ω  ρ˙ 1  ρ˙ N ; Ω 

U 

N

∑ mρ˙ i2 F

i 1

1 3

1 ˙2 WΩ 2

1

Ω ρ 1  Ω 3 ρ N X PΩ

(94)

It should be noted that the kinetic energy associated with the motion of the particles does not contain a ˙ The Lagrangian 94 was first written down in a seminal paper by H.C. Andersen [Andersen 1980]. term Ω. The corresponding Lagrangian equations of motion for this system are:

∂U ∂ ρi mΩ /

˙ 2Ω ρ˙ 3Ω i

1

ρ¨ i

¨ WΩ

1 3Ω

Pint





2 3

2

Ω3

N

N

i 1

i 1

∑ mρ˙ i2  ∑ ρ i 

(95)

∂U ∂ ρi 1

P 

P 

(96)

We see, that the changes in coordinates are now coupled to volume changes and vice versa. The equations of motion for the particle coordinates are somewhat more complex (a small price to be paid) and involve the velocities as well. The equation of motion for the volume is a second order differential equation. The right hand side balances internal and externally applied pressure. The mass W, which has dimensions of mass … length , 4 , determines the rate at which the system responds to deviations from the equilibrium pressure P. It can be regarded as the mass of a piston, which can move simultaneously in all directions in response to a change in pressure. The momentum π i conjugate to the variable ρ i is:

πi

∂§ ∂ ρ˙ i

2

Ω 3 mρ˙ i

(97)

˙ Hence the Hamiltonian is: The momentum Π conjugate to the volume is simply Π W Ω. 

N



1 2mΩ

2 3

∑ πi2 F

i 1

Π2 2W F

U  Ω 3 ρ 1  Ω 3 ρ N  1

26

1

F

PΩ

(98)

Therefore the Hamiltonian equations are:



π˙ i

πi

ρ˙ i

˙ Ω

˙ Π

(99)

2

mΩ 3 ∂U ∂ ρi Π W / 1 3Ω

(100) (101)

π2

N

∂U ∂ ρi 1

N

∑ mΩi 23  ∑ ρ i 

i 1

i 1

P 

(102)

which form a set of 2dN F 2 canonical equations, which can be solved given initial values of the coordinates, momenta, volume and rate of change of the volume. Along the phase trajectories in the dN F 2dimensional space the Hamiltonian is conserved. Hence the partition function of the system can (apart from an unimportant multiplicative factor) be written as: ∆  N  P 





0 M

/



dΩ



N

πi2



δ

2

i  1 2mΩ 3







dΩ



δ HF

Π2 2W



6



d ρ 1  F

U F PΩ F d ρ 1 





0



dρ N



Π2 2W

dρ N

7



d π 1 







0

1

d π 1 



dπ N

dπ N (103) (104)

where H E F PV is the enthalpy of the system. Now the contribution of the term Π 2 ) 2W to the energy is on the average k B T ) 2, so that, in the constant pressure ensemble the enthalpy H is conserved to within a factor kB T ) 2. In the thermodynamic limit this factor is negligible, so that ∆  N  P  0  Ž ∆  N  P H  , the partition function of the constant pressure, constant enthalpy ensemble. Thus the Andersen Lagrangian generates trajectories consistent with an NPH ensemble and we obtain corresponding NPH-ensemble averages. K For example, for the volume fluctations we obtain:

κS

!

1 ∂Ω7 Ω 6 ∂P

S



δ Ω2 L  kB T Ω 

(105)

The equations of motion can be converted back to real rather than scaled coordinates, which has some advantages over the formulation given here. For detail regarding this transformation we refer to [Clarke 1987]. The technique proposed by Andersen allows the volume Ω to fluctuate in a molecular dynamics simulation, but the shape of the cell remains the same. A powerful generalization of Andersen’s technique of constant pressure simulations has been proposed by Parrinello and Rahman [Parrinello 1980], which allows for changes in shape of the simulation cell. This technique is particularly well suited to investigate structural phase transitions. It has been used extensively to investigate structural stability of solids and polymorphic transitions [Parrinello 1981]. Consider a system of N particles with positions r i in a (in general triclinic) cell, which is periodically repeated to form a Bravais lattice. The elementary translation vectors of the elementary cell are a  b  c. The cell is shown in figure 7.2. We now consider a non-orthogonal coordinate system with axis along the translation vectors a  b  c respectively. Then we may write the coordinates r i  xi  yi  zi  of each particle in terms of its components ρ i  ξi  ζi  ηi  along each of the coordinate-axis: ri



27

ρi

(106)

µ

µ µ ³

µ

µ¶

³ µ µ ³

³

c µ ³b

³T´

µ

µ

µ

µ

² ³

µ

µ

µ

µ

µ ³

³ µ µ ³

µ

³

³

µ

µ µ

µ

a

Figure 5: Parrinello-Rahman MD-cell where the elements of the matrix ± are the components of the vectors a  b  c. Thus ax ay az

±J ¸·¹

bx by bz

cx cy cz

(107) º»

The distance ri j between two particles can be written in terms of ri2j

rTij ri j

Ž

ρi j

which defines the metric tensor ¼ . Here elements: ¼

·¹

T

T ¼

ρi jT

±

T

±

and ρ i j as:

ρi j ±

ρij

denotes transpose. Obviously a2 a  b a  c a  b b2 b  c a  c b  c c2

¼

is a symmetric tensor with

º»

Instead of the volume Ω a  b … c we now consider each of the translation vectors as dynamical variables. By analogy with equation 94, we postulate the following Lagrangian:

§

1 N T¼ ρ˙ i ρ˙ i F W  a˙ 2 F b˙ 2 F c˙ 2  2 i∑  1

U  ± ρ 1  ± ρ N  P a  b … c

(108)

The standard procedure then yields the following equations of motion:

ρ¨ i

1 m± 

W b¨



¼ , 1¼˙

ρ˙ i

(109)

mξ˙i ± ρ˙ i  ξi  ±

, 1 T 

∂U 7 ∂ ρi 

P  b … c

(110)



mζ˙i ± ρ˙ i  ζi  ±

, 1 T 

∂U 7 ∂ ρi 

P  c … a

(111)

i 1 6 N



∂ρ i 



N

W a¨

, 1 ∂U

i 1 6

28

N

W c¨



i 1 6

mη˙ i ± ρ˙ i  ηi  ±

, 1 T 

∂U 7 ∂ ρi 

P  a … b

(112)

These form a set of 3  N F 3  coupled differential equations for the dynamics of the particles and the cell-axis. The dynamics of the cell-axis is described by a total of 9 degrees of freedom. On average a kinetic energy of 9 ) 2k BT is associated with these degrees of freedom, so that, to within a factor of 9 ) 2k B T , the enthalpy of the system is conserved. The Parrinello- Rahman MD-technique is extremely useful in the study of structural stability of solids and structural phase transitions in solid crystalline materials. For the study of liquids it is less useful.

7.3 Constant temperature simulations Since pressure is a mechanical quantity there is a natural way to introduce a term in the Hamiltonian or Lagrangian, which describes the energy associated with a change in the volume variable, namely the thermodynamic work PΩ of expansion against a fixed external pressure P. The coupling with a temperature bath, so that we can simulate at constant temperature, is not so straightforward. An elegant way to introduce such a coupling was first proposed by Suchi Nos´e [Nos´e 1984a, Nos´e 1984b] in 1984. Consider a system of N particles in a volume Ω interacting through some potential function U  r 1  rN  . As usual we consider for simplicity a system of structureless particles. Let us now image a virtual system, which is related to the real system through a time scaling factor s. We denote quantities in the real system with a prime, whereas the corresponding quantities in the virtual system are unprimed. The correspondence between the two sets is given below: real ri

dt 

r˙ i

9

irtual ri dt s s˙ri

(113) (114) (115)

As with the Andersen Lagrangian we consider the scaling factor s to be a dynamical variable, which effects the mapping (in time) between the real and the virtual ensemble. Hence we associate a kinetic energy 1 2  2 Qs˙ with the scaling variable s. Further we associate a potential enegy gk B T log s  with the variable s. 2 Q is a fictive mass, with dimension mass … length , T represents the temperature of a heat bath and g is a parameter to be determined later. We now postulate the following Lagrangian: §



ms2 N 2 1 2 Qs˙  U  r1  rN  gkB T log  s  r˙i F 2 i∑ 2  1

r1  rN  s  r˙ 1  r˙ N ; s˙+

(116)

Here the time-derivatives are taken in the virtual system. Let us first derive equations of motion in the virtual and in the real systems. In the second stage we shall show that, if we choose g f F 1, where f is the number of degrees of freedom ((3N  3) in a 3d-system), the Lagrangian 116 generates states corresponding to the canonical ensemble. The equations of motion in the virtual system are obtained in standard fashion from the Lagrangian 116 to yield:

m¨ri

Qs¨



∂U ∂ ri 

s˙ 2m r˙ i s

ms2 ∑Ni 1 r˙i2  gkB T s

(117) (118)

Here the dot denotes differentiation w.r.t. virtual time t. The last equation balances the kinetic energy of the system with f degrees of freedom with the thermal energy gk B T . This already suggests that g is strongly related to f . Consider first a real time average A¯ of some physical quantity A  r i  rN  pi  pN  , which is defined by: 29



1 T



T

T

dt A s’ t

0

T



dt  A  t  K 

0

0

ˆ

dt 1s

K

AL s 1L s



(119)

Note that, since s varies with time, the dynamics given above does not generate a set of equidistant states of the system. For computing time correlation functions it is absolutely necessary to have states equidistant in time. This can be done by interpolation. A more straightforward technique is to rewrite the differential equations in real time t  . We use d dt

dt  d dt dt 

1 d s dt 

whence d2 dt 2

1 d 1 d s dt  s dt 

1 ds d s3 dt  dt 

#

1 d2 s2 dt  2 F

This leads to the following equations of motion:

m¨i

∂U ∂ ri / 

s˙ m r˙ i s 

(120)

N

Qs¨

s m ∑ r˙i2  gkB T

i 1

F 1

1 2 s˙2 s

(121)

where the dot now denotes differentiation w.r.t. time in the real system (i.e. w.r.t. t  ). Let us now consider the partition function in the Nose ensemble. The conjugate momenta are:

pi

ps

∂§ ∂ r˙ i ∂§ ∂s

ms2 r˙ i

(122)



Qs˙

(123)

The transformation to real momenta p i is 

The Nose Hamiltonian 



sp i

(124)

ps

sp s

(125)

is:

Nose

N



pi

p2

p2s 2Q

∑ 2msi 2 F

Nose

i 1

U  r1  rN  F

F

gkB T logs

(126)

For the dynamics resulting from the equations of motion 95 the Hamiltonian 126 is a conserved quantity. Similarly the linear momenta P ∑Ni 1 pi are conserved. Hence the partition function Q Nose becomes: 

QNose



d ps



ds



dp1 



dpN



dr1 

drN δ  

Nose 

ENose  δ  P 

(127)

where we have set the total momentum zero. Note that, because of the conservation of momentum, only f 3N  3 momenta are independent and the integration is effectively only over f momenta. We now transform to real momenta and real time to get:

30

 /

δ

QNose ½









d p s

ds



dp 1  p 2s 2Q

p 1  p N  r 1  r N F



dp N

dr  1 

dr N s f =

1

gkB T logs  ENose δ  P  F

(128)

1

is the Hamiltonian of the real system. The integration over the variable s can now be carried Here  out. The δ -function ensures, that only the term –F

s exp

g

contributes to the integral and there are f f F 1 the partition function becomes:

QNose

ENose 7 kB T

exp

6 …



exp 

–F ·¹¿

2π QkB T 

·¹~ F

ENose

gkB T

(129) º»

1 factors s occurring in the integrand. Hence if we choose





dp s 

p 2s  2Q 

exp 6





dp 1 

dp N



ENose

kB t 3 2

2

p¾ s 2Q 

º»

δ P







dr 1  

dr N 



ENose 7 Q N  Ω T  kB T

(130)

so that, apart from a multiplicative constant, which is of no importance, the partition function of the Nose ensemble is identical to the partition function of the NΩT ensemble. Similarly one can show, that ensemble averages are identical to canonical ensemble averages. Hoover [Hoover 1985] has given a more general formulation of constant temperature simulations.

8

Constraint dynamics

8.1 Why constraint dynamics? Let us onsider the case, that the system, whose dynamics we wish to investigate, consists of molecules. Each molecule is composed of atoms joined together by intermolecular bonds. The forces associated with these bonds are so strong, that the molecule cannot dissociate. The molecules interact through some intermolecular potential, which usually is obtained by summing over contributions of atomic building blocks of the molecule. There are thus two parts, an intermolecular interacting coming from groups on different molecules and an intramolecular interaction coming from groups on the same molecule. The equations of motion can now be integrated in the same way as before to calculate various quantities of interest. There are two problems however. The interatomic forces, associated with bonding, are often so strong that the the typical frequencies associated with that motion (vibrational modes) are much higher than the frequencies associated with translational or rotational motion. Hence these motions are almost completely decoupled from the other motions in the system. To integrate the equations of motion accurately one now requires a small time-step, consistent with the fast vibrational motions in the molecule. This means that large numbers of time-steps are required to study translational and rotational motion, making the procedure highly inefficient. The second problem is more fundamental. Since the vibrational modes have such high frequencies the should not be treated classically, but quantummechanically. An alternative to the above mentioned approach is constraint dynamics, in which the degrees of freedom associated with interatomic bonding are completely frozen out. The atomic bonds are treated as holonomic constraints, which fix certain bondlength’s and, if necessary, bondangles. This means, that our molecule now consists of a set of atoms with coordinates r il , with i 1  n denoting the atom in molecule l, with l 1  N. In addition there are now a set holonomic constraints of the form: 31

fβl  r1l  rnl 

0

(131)

with k 1  K. The constraints describe the interdependency of the coordinates in a molecule. Thus a molecule now only has f 3n  K degrees of freedom. Textbooks on Classical Mechanics [Goldstein 1980] now show, that it is always possible to find a suitable set of generalized coordinates q 1  q f , in which the constraint equations 131 have been used to eliminate K variables. The remaining f are now independent. Thus: ril

ril % q1l  q f l ; fαl 

(132) '

The constraints are now contained implicitly in the generalized coordinates. These are then used to construct a Lagrangian: §À%

q1  q f  q˙1  q˙ f

'

Kc % q1  q f  q˙1  q˙ f '



U % q1  q f '

(133)

from which Lagrangian equations of motion can be derived as discussed earlier. These can be integrated to yield the dynamic behaviour. There are several drawbacks however. 8

8

Firstly the relation between generalized and Cartesian coordinates is in general far from simple, except for the simplest types of molecules or completely rigid molecules.

8

Even for simple molecules the equations of motion in generalized coordinates contain singularities. Only for rigid molecules a singularity-free algorithm has been constructed, based on the quarternion representation [Evans 1980]. This cannot treat the general case of semi-flexible molecules however. Usually the kinetic energy Kc , when expressed in terms of generalized coordinates, contains both q and q. ˙ Therefore algorithms, such as the Verlet scheme, cannot be used.

An alternative is to use constraint forces, which act on the 3n coordinates of a molecule in such a way, that the constraints are satisfied at all times. To see how this works, we first consider a simple example. The technique of constraint dynamics was introduced by Ryckaert et. al. [Ryckaert 1977] for the simulation of molecular systems. A general formulation of the problem was given by Ciccotti and Ryckaert in 1986 [Ciccotti 1986]. More recently de Leeuw et. al. [deLeeuw 1990] have given a formulation based on Hamiltonian mechanics.

8.2 Dynamics of Stockmayer molecules A simple model for polar molecules has been proposed by Stockmayer. A Stockmayer molecule is a Lennard-Jones atom with an embedded dipole moment µ . Thus the interaction between two Stockmayer molecules is given by:

ϕS  µ 1  µ 2  r12 + ϕLJ  r12  F

ϕdd  µ 1  µ 2  r12 

(134)

where ϕLJ is the Lennard-Jones potential equation 10 and

µ1  µ2 3  r12 

ϕdd  µ 1  µ 2  r12 M

µ 1  r12   µ 2  r12 5  r12 

(135)



µ2  2 cos θ1 cos θ2 F sin θ1 sin θ2 cos φ12  3 r12

(136)



µ2  D 1  2 3 r12

(137)









3

where the angles θ1  θ2  φ12 are defined in figure 8.2. The angular function D  1  2  is rotationally invariant. Clearly the θ i  φi are the generalized coordinates describing the orientation of the dipoles. The Lagrangian is: 32

µ

µ

µ

µ

µ

µ¶

ÁÂ

θ1

r12

Á

² Á

θ2

ÃÄ

Ãφ

ÅÃ Æ

Å 12 Å

Á

Figure 6: Dipolar interactions

§

1 N 2 1 2 N ˙2 2 m r˙i F I µ ∑  φi sin θi F θ˙i2  2 i∑ 2  1 i 1 

Us  µ i  



ri  

(138)

Here I is the moment of inertia of the molecules. The Lagrangian leads to the following equations of motion for the angles φ i :

φ¨i

∂ ϕdd sin θi  ∂ φi 1

#

(139)

2

Clearly these equations have a singularity for θ i 0. This problem can be overcome by transforming to a different coordinate system each time sin θ i becomes small. This has been used by Rahman and Stillinger [Rahman 1971] in their pioneering work on water. However, this is very inconvenient and involves a lot of unnecessary bookkeeping. An alternative approach is to consider the Cartesian components of the dipole moment vectors as dynamical variables, with the restrction that the length of the dipole moment vector must remain constant. This constraint can be incorporated in the Lagrangian using the method of undetermined multipliers. Thus the Lagrangian becomes (for a system of N dipoles): §

1 N  2 m˙ri F I µ˙ i2  VS  r1  rN ; µ 1  µ N  2 i∑  1 F

1 N  2 λ i µi  µ 2  2 i∑  1

(140)

The λi are multipliers to be determined later. The equations of motion become:

m¨ri



I µ¨ i



∂ VS ∂ ri ∂ VS ∂ µi

(141) F

λi µ i

(142)

The last term in equation 127 is a force along the direction of the dipole vector, which enforces the constraint of constant length µ . To determine the multiplier λ i we note, that the constraint µ i2 µ 2 must be kept at all times, so that differentiation gives:

µ˙ i  µ i µ¨ i  µ i F µ i

2



0

(143)



0

(144)

The first equation tells us, that the velocity µ˙ i cannot have a component along µ i , otherwise it would elongate the dipole moment vector. The second equation is the equivalent of centrifugal force, enforcing

33

the constancy of the magnitude of the dipole moment. This equation can be used to eliminate the multiplier λi . We multiply the equation of motion 141 with µ i and substitute in the second derivative of the constraint equation. This gives:

λi

1 µi2

∂ VS 7 ∂ µi

I µ˙ i2  µ i  6

(145)

We may now integrate the equations of motion for our system. There is one additional problem, however. The constraint equations of motion are unstable, so that numerical errors, which take the system off the constraint surface, with increase exponentially in time. Hence the constraints have to be monitored and, as soon as the deviation becomes larger that a preset tolerance ε , enforced. This is done simple by setting

µ˙ i 4

µ˙ i 

µi 4

µi



µ˙ i  µˆ i  µˆ i µ2 µi2

…ÈÇ

Note, that only for simple molecules, such as the Stockmayer system or diatomics, the multipliers λ i can be evaluated explicitly. In all other cases considerably more work is involved. This will be treated in the next section.

8.3 General case Let us now consider a system composed of N molecules. Each molecule consists of n subunits, which are connected though M constraints. The constraints are generally of the form:  rα

fαl  r1l  rnl +

rαjl 

il 

2

dα2 



0

(146)

Here l labels the molecules, l 1  N and i  j the subunits in molecule l. The constraint keeps the distance between the subunits i and j fixed. Both bondlength’s and bondangles can be fixed in this manner. The subunits can be atoms of groups of atoms (unit atom model). For example in simulating organic compounds CH3  groups are generally treated as a single Lennard-Jones atom. The Lagrangian for this system is: §

1 N n ∑ mir˙il2 2 l∑ i  1  1 

U



N

ril 

 F

M

∑ ∑ λαl fαl

(147)

l 1 α  1

where U is the potential energy. The equations of motion are:

∂ fl

M

mi r¨ il

∑ λαl ∂ rα

Fil F

α 1

(148)

il

Fil ¡ ∂ U ) ∂ ril is the force acting on particle atom i in molecule l. The last term is the constraint force. This is the force required to maintain the constraint. They must be kept at all times, so that:

d2 l f dt 2 β

∂ fβl

n

d l f dt β

∑ r˙ il 



n

∑ r¨ il

i 1 n



∂ ril

i 1



n

∂ fβl

∂ ril

∑ ∑ r˙ il 

i 1 j  1

0

(149)

F

∂ 2 fβl

∂ ril ∂ ril



r˙ il

To eliminate the constraints we now multiply equation 148 with ∂ f βl ) ∂ ril and sum over all i:

34

(150)

∂ fβl

n

∑ r¨ il 

n



∂ ril

i 1

M

n

α 1

i 1

F

l 1 ∂ fαl ∂ fβ i ∂ ril ∂ ril

∑ mil F ∑ λαl ∑ m

i 1

i

(151)

We define: n





i 1 j  1 n



i 1

∂ ril ∂ ril



r˙ il

(152)

∂ fβl

1

∑ m ril 

Pβl

∂ 2 fβl

n

∑ ∑ r˙ il

Tβl

(153)

∂ ril

i

l 1 ∂ fαl ∂ fβ ∑  i  1 mi ∂ ril ∂ ril n

l Mαβ



(154)

so that, in matrix notation: 

Tβl

Pβl 

M

l ∑ λαl Mαβ



(155)

α 1

which can be solved for the multipliers λ αl . Clearly Tl and Pl are vectors of dimension M and matrix of dimension M … M. It is easily shown, that É l is a positive definite matrix, since: / M

M

∑∑

α 1β  1

∂ fl ∑ xα ∂ rα il 1 α 1

n

M

1 ∑m i 1 i

l xα xβ Mαβ

É

l

is a

2

<

0

for any vector x. Hence ÉVÊ can always be inverted. Note however, that the dynamical equations are unstable w.r.t. the constraints. Thus any numerical error made while integrating these equations will grow and eventually dominate the behaviour of the solutions. To cure this one has to monitor the solutions and check, whether the constraint equations are satisfied within a prescribed tolerance δ . If not, one has to reset the coordinates in such a way, that the velocity constraints (eq. 134) as well as the constraint equations for the coordinates ?? are satisfied exactly. Consider first the constraint equations and let us assume, that f αl  r1l  rnl  deviates from 0 by a small amount. We wish to project the coordinates back onto the constraint surface, which implies a small correction of the form: M

ril

4

ril

ril F



β 1

εβl

∂ fβl

∂ ril

(156)

Inserting this into the constraints and expanding these equations to first order in ε βl yields: M

∑ εβl É

β 1

where

É 

l

  β α !

fαl  r1l  rnl 

(157)

is a matrix with elements:  É

l

  βα

n

∂ fβl ∂ fαl  il ∂ ril 1

∑ ∂r

i

(158)

Note the similarity between the matrix É defined in equation 139 and É  . Clearly É  is a symmetric positive definite matrix. The equations can be solved for ε β to correct the constraints. If necessary an iterative procedure can be used, as the procedure is not exact. For sufficiently small tolerance δ this is not required however. The velocity constraints can be satisfied exactly with a similar procedure. Suppose n

∑ r˙ il 

i 1

∂ fαl ∂ ril 35



ζαl

(159)

We set

∂ fβl

M

r˙ il

4

r˙ il

∑ ηβl ∂ r



ril F

β 1

(160)

il

and insert this into the equations for the velocity constraints. Then we obtain: M

∑ ηβl  É

l

  β α !

β 1

ζαl

(161)

which can be solved for the η βl . Note, that although the same matrix É  appears in the equations for the velocity and coordinate constraints, this matrix has to be evaluated for a different set of coordinates. An alternative procedure to the constraint dynamics described above is the SHAKE procedure, first proposed by Berendsen and coworkers [van Gunsteren 1977, van Gunsteren 1980]. This works particularly well with the Verlet integration scheme. Let us insert the right hand side of equation 148 into Verlet’s algorithm. Then we obtain: /

ril  t F δ t Ë

2ril  t X ril  t  δ t  r il  t F δ t 



F

δ t2 mi F

δ t2 M α

∂ fαl 1 ∂ ril 1



α

∂ 1 ∂ ril



mi

M

Fil F

fαl

The positions r  il  t F δ t  are those obtained if the constraint were not present; the effect of the constraint forces is entirely contained in the last term, which will project these positions back onto the constraint surface. Rather than solving directly for the undetermined multipliers as described above we now determine the multipliers by the requirement, that at time t F δ t the constraints must be satisfied exactly. Thus we propagate the particles to new positions r  il  t F δ t  , which are obtained from intermolecular forces only, and then require, that: /

fαl



r il  t F δ t  F

δ t 2 M ∂ fαl mi α∑  1 ∂ ril 

1

0

(162)

Since the constraint equations are in general quadratic the provide a set of non-linear equations for the λαl , which can be solved iteratively. The SHAKE procedure does this by considering each constraint in turn and satisfying that constraint exactly. This procedure is then repeated until at last all constraints are satisfied to within a prescribed tolerance. Although simple and effective this procedure may require a large number of iterations if some constraints are violated by a large amount.

8.4 Constrained and unconstrained averages Finally we consider the partition function, corresponding to the constrained ensemble. Surprisingly it will turn out, that the constrained system cannot be obtained as the limit of an arbritrary stiff system. Thus averages computed in a system with hard constraints are not necessarily the same as averages computed in a system, where constraints have been replaced by springs with arbitrary large spring constants. Consider the trimer as sketched in fig. 8.4. In fig. 8.4a we have sketched a fully flexible trimer, in which the bondlength’s r12 and r13 are kept close to their equilibrium values d by a harmonic force with force-constant k, so that Vh

k 2



r12  d 

2

F



r13  d 

The probability distribution P  Ψ  of the bondangle Ψ is Ps  Ψ 

1 sin Ψ 2 36

2



Ï

Ï

Ï

Ï

ÌÍ

Ì

Ì

Ì

a

Ì ÏÁ

Ψ

Á Á

Á

ÁÎ Ï

b

Figure 7: Symmetric trimer with bondlength d. In (a) the bonds are represented by springs; in (b) the bonds are represented by hard constraints. In the case sketched in fig. 8.4(b), where the bonds are rigidly constrained, the probability distribution is: 1 cos2 Ψ sin Ψ  1  2 4

Pc  Ψ 

so that any average involving the angle Ψ will give different answers for the two cases. In the worst case (cos Ψ 1) the ratio of the two probability distributions is 0 * 86. As this corresponds to the case where P  Ψ  tends to zero the difference is in general no too serious. To see how this difference arises we consider quite generally a system with N particles and M constraints. In our theoretical analysis we shall use generalized coordinates, denoted by q, such that 3N  M coordinates are independent. These correspond to the soft degrees of freedom and are denoted by q S . The remaining M coordinates are choosen identical to the constraints q H fα   α 1  M. Thus we have ri

ri  qS 





qH  

If the constraints are enforced rigidly the potential energy will be a function of the soft coordinates q S and depend parametrically on the hard coordinates f. We now express the Lagrangian § in terms of these coordinates:



1 N mi r˙ 2i  U 2 i∑  1

3N 3N ∂ r ∂ ri 1 N q˙ mi ∑ ∑ q˙k i  ∑ 2 i  1 k  1 l  1 ∂ qk ∂ ql l

§

Ž

qT ¼ q

(163) (164)

where we have defined the metric tensor ¼ as:

∂r

N

Gkl

∑ mi ∂ q i

i 1

k

The corresponding generalized momenta are: 37



∂ ri ∂ ql

(165)

pk

∂§ ∂ q˙k

3N

∑ Gkl q˙l



(166)

l 1

so that 3N

q˙k

∑ Gkl, 1 pl



(167)

l 1

The Hamiltonian of our system is therefore: 

1 3N 3N ∑ pk Gkl, 1 pl F U  q1  q3N  2 k∑  1 l 1

1 T¼ p 2 Ž

, 1

p F U  q

(168)

The canonical distribution function of our system is then:

ρ  p  q 

exp   β  dp dq exp 



p  q  β   p  q  

(169)

The form remains exactly the same, because in a canonical tranformation, such as given above, the Jacobian J  pr  r; p  q  of the transformation is unity. It follows that the partition function: 

Q  N  V  T 



dq exp 

dp



β 

p  q 

also retains the same form and remains unaltered. Let us now look at the configurational distribution function ρ  q  , which is obtained by integration over all momenta. Now consider the integral: 

I where Ð that



dp exp 



1 3N β pk Akl pl  2 k∑ ¢ l 1 

Akl is a positive definite symmetric matrix. The transformation P ÒÑ

Ñ

T

Рт #Ó

, 1p

diagonalizes Ð , so

(170)

where Ó is the unit matrix. Since Ð is a positive definite symmetric matrix such a transformation is always possible. Then 

I

/

J  P  p  dP exp 

3N



N

1 Pk2 +

β 2 k∑  1

∏  2π mikB T  i 1

3 2

1

 ÔC

where J  P  p +  ÑÕ is the Jacobian of the transformation. But, because of equation 169 we must have, that  ÑÖ 2  Ð & 1, whence /

N

I

∏  2π mikBT 

3 2

:

Ð 

1

i 1

, 1

(171)

and we obtain the result, that:

ρ  q  c : 

¼ 

exp  

β U  q 

(172)

where c is a normalization constant. Thus far we haven’t enforced any constraints. If we do so, the coordinates q H and the corresponding velocities are simply remove from the Lagrangian, so that §



qS  q˙ S 

1 S q˙  2

T¼ S S





U  q

where q˙ S is a vector with elements q˙ Sl and the metric tensor is now given by: 38

(173)



∂r

N

GS 

∂ ri ∂ qSl

∑ mi ∂ qSi 

kl

i 1

k

(174)

Clearly ¼ S is a matrix of size f … f , with f 3N  M. It is straightforward to show, that positive definite matrix. The corresponding generalized momenta are: pSk

∂§ ∂ q˙Sk

¼ S

is also a

f

∑ GSkl q˙Sl



(175)

l 1

whence, for the constrained system, 1 S T ¼ S , 1 S p F U p    2 The corresponding probability distribution in phase space is: 

c



pS  qS 

exp 

ρc  pS  qS 

(176)

β   pS  qS  QS  N  Ω  T  

(177)

The configurational distribution function is: 

ρc  qS M

c



c

1 β  pS  2

dpS exp : 

¼ S

  exp

6

T

¼ S , 1 S p F  

U  qS 

β U  qS  

7

(178)

¼ —   . In particular, if we consider the probability which is not the same as equation 171, since  ¼ S  × distribution for the unconstrained system, in which the hard coordinates have been given their constrained values we see, that:

ρc  qS   S ρ q  qH fα  



¼ S 

Ç

¼ 



—



1

(179)

It has been suggested in the literature [Fixman 1974, Fixman 1974] to compensate for this by introducing a term Uconstr

¼ S 

kB T log Ç

!



¼



(180)



in the potential. This requires the calculation of the ratio of two determinants. This can be done rather efficiently without evaluating the determinants separately. To see this consider first the inverse of ¼ : Gkl, 1

N

1 ∂ qk ∂ ql  i ∂ ri ∂ ri

∑m



i 1

(181)

This is easily verified, since: / , 1 Gml ∑ Gkm

∑m

∑ ∂ rk 

i¢ j

m

We now divide

¼

and its inverse

1 ∂ qk i ∂ ri



¼ , 1

∂q

i

i





∂ ri ∂ ql

m



∂rj ∂ qm mj ∂ ri ∂ qm 1 

∂rj ∂ ql

δkl

into blocks corresponding to the soft and hard coordinates. Thus ¼

¼ S 6

Ð

HS

39

Ð SH 7

Ð HH

(182)

and SS

¼ , 1

where

Ú

is an M

…



SH

HS

6ÙØ

7

Ú Ø

(183)

Ø

M matrix with elements:

H 1 ∂ qH k ∂ ql  ∂ ri i ∂ ri

N

∑m

Hkl

i 1

(184)

Consider then the matrix Û with elements: ¼ S Û

Ü

Ð HS 6

7

(185)

Ó

By straightforward matrix multiplication we have that: ¼¼ , 1

Û

Û SS

¼

6 Ø Ø ¼



6

Ø

¼

HS

Ø

6

7 Ó

SH

Ú Ð HS

Ø

Ü

Ð HS

SH Ð HS

SH

Ø

7

Ú

7

Ú Ø

¼ S 7

Ú

SS ¼ S F HS ¼ S F

ÓØ Ü

6

SH

Therefore: 

¼ S

s f X ‡ „ ¼

& !

Ý

Ú

4 



Ú



¼ S  

¼



(186)



and we have that:

ρ  q  „ Ú In terms of the constraint equations he elements of N

Hαβ

,  Ú

1 2

ρc  q 

are:

1 ∂ fα ∂ fβ  ∂r i ∂ ri i

∑m



i 1

(187)

(188)

When applied to the case of the flexible trimer it is easily shown, that: 

Ú 

,

1 2



2 cos2 Ψ  1  m 4

(189)

leading to the result mentioned earlier. The general conclusion therefore is, that cosntrained averages are not necessarily the same as averages in the corresponding very stiff system, and one should be careful. Fortunately in most simulations only bondlenght’s are constrained and bondangles are accounted for through some angular potential. In that case the difference between the two averages appears to be not too serious.

References [Alder 1957]

B.J. Alder and T.M. Wainwright, Phase Transition in the Hard Sphere System, J.Chem. Phys. 27, 1208-1209, (1957)

[Rahman 1964]

A. Rahman, Correlations in the motion of liquid argon, Phys. Rev. 136A, 405-411 (1964) 40

[Allen 1987]

M.P. Allen and D.J. Tildesley, Computer Simulation of Liquids, (Clarendon, Oxford, 1987).

[Landau 1980]

L.P. Landau and I.M. Lifshits, Statistical Physics. A Course of Theoretical Physics Vol VI, 2nd edition, Pergamon Press, Oxford, 1980.

[Hansen 1986]

J.P. Hansen and I.R. McDonald, Theory of Simple Lquids, 2nd edition, Academic Press, London, 1986.

[Lebowitz 1967]

J.L. Lebowitz, J.K. Percus and L. Verlet, Ensemble dependence of fluctuations with application to machine calculations, Phys. Rev. 253, 250-254 (1967)

[Chandler 1987]

D. Chandler, Introduction to Modern Statistical Mechanics, Oxford University Press, New York, 1987.

[Feynman 1972]

R.P. Feynman Statistical Mechanics, Benjamin, Reading(Mass.), 1972.

[Widom 1963]

B. Widom, Some topics in the theory of liquids, J. Chem. Phys. 39, 2808 (1963)

[Verlet 1967]

L. Verlet, Computer ’experiments’ on classical liquids. I. Thermodynamical properties of Lennard-Jones molecules, Phys. Rev. 159, 98-103 (1967)

[Verlet 1968]

L. Verlet, Computer ’experiments’ on classical liquids. II. Equilibrium correlation functions, Phys. Rev. 165, 201-214 (1968)

[Gear 1971]

C.W. Gear, The numerical initial value problems in ordinary differential equations, Prentice Hall, Englewood Cliffs, NJ, 1971.

[Swope 1982]

W.C. Swope, H.C. Andersen, P.H. Berens and K.R. Wilson, A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: application to small water clusters, J. Chem. Phys. 76, 637-649 (1982)

[Whittaker 1926]

E.T. Whittaker and G.N. Watson, A course in modern analysis, Cambridge University Press, 4th edition, 1978, Chapter 21.

[deLeeuw 1980]

S.W. de Leeuw, J.W. Perram and E.R. Smith, Computer simulations of electrostatic systems in periodic boundary conditions. I. Lattice sums and dielectric constant, Proc. Royal Soc. A373, 227-256 (1980)

[deLeeuw 1980]

S.W. de Leeuw, J.W. Perram and E.R. Smith, Computer simulations of electrostatic systems in periodic boundary conditions. II. Equivalence of boundary conditions, Proc. Royal Soc. A373, 257-264 (1980)

[deLeeuw 1980]

S.W. de Leeuw, J.W. Perram and E.R. Smith, Computer simulations of electrostatic systems in periodic boundary conditions., Ann. Rev. Phys. Chem. 36, 245-270 (186).

[Ewald 1921]

P.P. Ewald, Die Berechnung optischer und electrostatischer Gitterpotentiale, Ann. Phys 64 253-287 (1921)

[de Leeuw 1981]

S.W. de Leeuw and J.W. Perram, Computer simulations of ionic systems. Influence of boundary conditions, Physica 107A, 179-189 (1981)

[Caillol 1994]

J.M. Caillol, Comments on the numerical simulation of ionic systems in periodic boundary conditions, J. Chem. Phys. 101, 6080-6090 (1994)

[Andersen 1980]

H.C. Andersen, Molecular dynamics at constant temperature and pressure, J. Chem. Phys. 72, 2384 (1980)

[Clarke 1987]

J.H.R. Clarke and D. Heyes, Constant pressure simulations for molecular systems, Molec. simulation 2, 75-89 (1987) 41

[Parrinello 1980]

M.P. Parrinello and A. Rahman, Crystal structure and pair potentials: a molecular dynamics study, Phys. Rev. Lett. 45, 1196-1199 (1980)

[Parrinello 1981]

M.P. Parrinello and A. Rahman, Polymorphic transitions in single crystals: a new molecular dynamics method, J. Appl. Phys. 52, 7182-7190 (1981)

[Nos´e 1984a]

S. Nos´e, A molecular dynamics method for simulations in the canonical ensemble, Molec. Phys. 52, 255-268 (1984)

[Nos´e 1984b]

S. Nos´e, A molecular dynamics method for simulations in the canonical ensemble, J. Chem. Phys. 81, 511-520 (1984)

[Perram 1989]

J.W. Perram, H.G. Petersen and S.W. de Leeuw, An algorithm for the simulation of condensed matter which scales as 23 power of the number of particles, Molec. Phys. 67, 875-889 (1989)

[Onsager 1931a]

L. Onsager, 37, 405-417 (1931)

[Onsager 1931b]

L. Onsager, 38, 2265-2278 (1931)

[Alder 1970a]

B.J. Alder and T. E. Wrainwright, Decay of the velocity autocorrelation function, Phys. Rev. A1, 18-21 (1970)

[Alder 1970b]

B.J. Alder,D.M. Gass and T. E. Wrainwright, Studies in molecular dynamics VIII. The transport coefficients for a hard sphere fluid, J. Chem. Phys. 53, 3813-3826 (1970)

[Ernst 1971]

M.J. Ernst, E.H. Hauge and J.M.J. van Leeuwen, Asymptotic time behaviour of correlation functions. I. Kinetic terms, Phys. Rev. A4, 2055-2065 (1971)

[Evans 1980]

D.J. Evans and S. Murad A singularity-free algorithm for molecular dynamics simulation of rigid polyatomics, Molec. Phys. 34, 327-331 (1977)

[Goldstein 1980]

H. Goldstein, Classical Mechanics, 2nd ed. Addison-Wesley, Reading, Ma. 1980.

[Hoover 1985]

W.G. Hoover, Canonical dynamics: equilibrium phase space distributions, Phys. Rev. A31, 1695-1703 (1985)

[Ryckaert 1977]

J.P. Ryckaert and G. Ciccotti and H.J.C. Berendsen, Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes, J. Computational Physics 23, 327-341 (1977)

[Ciccotti 1986]

G. Ciccotti and J.P. Ryckaert, Molecular dynamics simulation of rigid molecules, Computer Physics Rep. 4, 345-392 (1985)

[Rahman 1971]

A. Rahman and F.H. Stillinger, Molecular dynamics study of liquid water, J. Chem. Phys. 55, 3336-3359 (1971)

[deLeeuw 1990]

S.W. de Leeuw, J.W. Perram and H.G. Petersen, Hamilton’s equations for Constrained systems, J. Stat. Phys. 61, 1203-1222 (1991)

[van Gunsteren 1977] W.F. van Gunsteren and H.J.C. Berendsen, Algorithms for macromolecular dynamics and constraint dynamics, Molec. Phys. 34, 1311-1327 (1977) [van Gunsteren 1980] W.F. van Gunsteren, Constraint dynamics of flexible molecules, Molec. Phys. 40, 1015-1019 (1980) [Petersen 1989]

H.G. Petersen and H. Flyvbjerg, Error estimates in molecular dynamics simulations, J. Chem. Phys. 91 461-467 (1989)

[Fixman 1974]

M. Fixman, Classical statistical mechanics for constraints: a theorem and application to polymers, Proc. nat Acad. Sci. 71, 3050-3053 (1974)

[Fixman 1974]

M. Fixman, Simulation of polymer dynamics. I. General theory, J. Chem. Phys. 69, 1527-1537 (1978)

42

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