may 2001d. ceperley simulation methods1 simulation techniques: outline lecture 1:...

May 2001 D. Ceperley Simulation Methods 1

Simulation Techniques: Outline

Lecture 1: Introduction:Statistical Mechanics, ergodicity, statistical errors.

Lecture 2: Molecular Dynamics: integrators, boundary conditions in space and time, long range potentials and neighbor tables, thermostats and constraints, single body and two body correlation functions, order parameters, dynamical properties.

Lecture 3: Monte Carlo Methods: MC vs. MD, Markov Chain MC, transition rules.

NB All material at beginner’s level. Interruptions welcomed!


Introduction to Simulation:Lecture 1

• Why do simulations?

• Some history

• Review of statistical mechanics

• Newton’s equations and ergodicity

• The FPU “experiment”

• How to get something from simulations: statistical errors


Why do simulations?

• Simulations are the only general method for “solving” many-body problems. Other methods involve approximations and experts.

• Experiment is limited and expensive. Simulations can complement the experiment.

• Simulations are easy even for complex systems.

• They scale up with the computer power.


Two Simulation Modes

A. Give us the phenomena and invent a model to mimic the problem. The semi-empirical approach. But one cannot reliably extrapolate the model away from the empirical data.

B. Maxwell, Boltzmann and Schoedinger gave us the model. All we must do is numerically solve the mathematical problem and determine the properties. (first principles or ab initio methods) This is what we will talk about.


“The general theory of quantum mechanics is now almost complete. The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.”

Dirac, 1929


Challenges of Simulation

Physical and mathematical underpinings:

• What approximations come in:

• Computer time is limitedfew particles for short periods of time. (space-time is 4d. Moore’s Law implies lengths and times will double every 6 years if O(N).)

• Systems with many particles and long time scales are problematical.

• Hamiltonian is unknown, until we solve the quantum many-body problem!

• How do we estimate errors? Statistical and systematic.

• How do we manage ever more complex codes?


Short history of Molecular Simulations• Metropolis, Rosenbluth, Teller (1953) Monte Carlo

Simulation of hard disks.

• Fermi, Pasta Ulam (1954) experiment on ergodicity

• Alder & Wainwright (1958) liquid-solid transition in hard spheres. “long time tails” (1970)

• Vineyard (1960) Radiation damage using MD

• Rahman (1964) liquid argon, water(1971)

• Verlet (1967) Correlation functions, ...

• Andersen, Rahman, Parrinello (1980) constant pressure MD

• Nose, Hoover, (1983) constant temperature thermostats.

• Car, Parrinello (1985) ab initio MD.


Molecular Dynamics (MD)

• Pick particles, masses and potential.

• Initialize positions and momentum. (boundary conditions in space and time)

• Solve F = m a to determine r(t), v(t).

Newton (1667-87)

• Compute properties along the trajectory

• Estimate errors.

• Try to use the simulation to answer physical questions.


What are the forces? • Crucial since V(q) determines the quality of result.

• In this lecture we will use semi-empirical potentials: potential is constructed on theoretical grounds but using some experimental data.

• Common examples are Lennard-Jones, Coulomb, embedded atom potentials. They are only good for simple materials. We will not discuss these potentials for reasons of time.

• The ab initio philosophy is that potentials are to be determined directly from quantum mechanics as needed.

• But computer power is not yet adequate in general.

• A powerful approach is to use simulations at one level to determine parameters at the next level.


Statistical Ensembles• Classical phase space is 6N variables (pi, qi) and a Hamiltonian

function H(q,p,t).• We may know a few constants of motion such as energy, number

of particles, volume... • Ergodic hypothesis: each state consistent with our knowledge is

equally “likely”; the microcanonical ensemble.• Implies the average value does not depend on initial conditions.• A system in contact with a heat bath at temperature T will be

distributed according to the canonical ensemble:

exp(-H(q,p)/kBT )/Z

• The momentum integrals can be performed. • Are systems in nature really ergodic? Not always!


Ergodicity• Fermi- Pasta- Ulam “experiment” (1954)

• 1-D anharmonic chain: V= [(q i+1-q i)2+ (q i+1-q i)3]

• The system was started out with energy with the lowest energy mode. Equipartition would imply that the energy would flow into the other modes.

• Systems at low temperatures never come into equilibrium. The energy sloshes back and forth between various modes forever.

• At higher temperature many-dimensional systems become ergodic.

• Area of non-linear dynamics devoted to these questions.


Let us say here that the results of our computations were, from the beginning, surprising us. Instead of a continuous flow of energy from the first mode to the higher modes, all of the problems show an entirely different behavior. … Instead of a gradual increase of all the higher modes, the energy is exchanged, essentially, among only a certain few. It is, therefore, very hard to observe the rate of “thermalization” or mixing in our problem, and this wa s the initial purpose of the calculation.

Fermi, Pasta, Ulam (1954)


• Equivalent to exponential divergence of trajectories, or sensitivity to initial conditions. (This is a blessing for numerical work. Why?)

• What we mean by ergodic is that after some interval of time the system is any state of the system is possible.

• Example: shuffle a card deck 10 times. Any of the 52! arrangements could occur with equal frequency.

• Aside from these mathematical questions, there is always a practical question of convergence. How do you judge if your results converged? There is no sure way. Why? Only “experimental” tests.– Occasionally do very long runs.

– Use different starting conditions.

– Shake up the system.

– Compare to experiment.


Statistical ensembles

• (E, V, N) microcanonical, constant volume

• (T, V, N) canonical, constant volume

• (T, P N) constant pressure

• (T, V , ) grand canonical

• Which is best? It depends on:– the question you are asking

– the simulation method: MC or MD (MC better for phase transitions)

– your code.

• Lots of work in last 2 decades on various ensembles.


Definition of Simulation

• What is a simulation?An internal state “S”

A rule for changing the state Sn+1 = T (Sn)

We repeat the iteration many time.

• Simulations can be– Deterministic (e.g. Newton’s equations=MD)

– Stochastic (Monte Carlo, Brownian motion,…)

• Typically they are ergodic: there is a correlation time T. for times much longer than that, all non-conserved properties are close to their average value. Used for:– Warm up period

– To get independent samples for computing errors.


Problems with estimating errors

• Any good simulation quotes systematic and statistical errors for anything important.

• Central limit theorem: after “enough” averaging, any statistical quantity approaches a normal distribution.

• One standard deviation means 2/3 of the time the correct answer is within of the estimate.

• Problem in simulations is that data is correlated in time. It takes a “correlation” time to be “ergodic”

• We must throw away the initial transient and block successive parts to estimate the mean value and error.

• The error and mean are simultaneously determined from the data. We need at least 20 independent data points.


Estimating Errors

Trace of A(t):

Equilibration time.

Histogram of values of A ( P(A) ).

Mean of A (a).

Variance of A ( v ).

estimate of the mean: A(t)/N

estimate of the variance,

Autocorrelation of A (C(t)).

Correlation time ( ).

The (estimated) error of the (estimated) mean ( ).

Efficiency [= 1/(CPU time * error 2)]


Data Spork

Interactive code to perform statistical analysis of dataShumway, Draeger,Ceperley


Statistical thinking is slippery

• “Shouldn’t the energy settle down to a constant” – NO. It fluctuates forever. It is the overall mean which converges.

• “My procedure is too complicated to compute errors”– NO. Run your whole code 10 times and compute the mean and

variance from the different runs

• “The cumulative energy has converged”.– BEWARE. Even pathological cases have smooth cumulative

energy curves.

• “Data set A differs from B by 2 error bars. Therefore it must be different”. – This is normal in 1 out of 10 cases.


Molecular Dynamics:Lecture 2

• What to choose in an integrator

• The Verlet algorithm

• Boundary Conditions in Space and time

• Neighbor tables

• Long ranged interactions

• Thermostats

• Constraints


Criteria for an Integrator

• simplicity (How long does it take to write and debug?)

• efficiency (How fast to advance a given system?)

• stability (what is the long-term energy conservation?)

• reliability (Can it handle a variety of temperatures, densities, potentials?)

• compare statistical errors (going as h-1/2 ) with time step errors which go as hp where p=2,3...?


Characteristics of simulations.

• Potentials are highly non-linear with discontinuous higher derivatives either at the origin or at the box edge.

• Small changes in accuracy lead to totally different trajectories. (the mixing or ergodic property)

• We need low accuracy because the potentials are not very realistic. Universality saves us: a badly integrated system is probably similar to our original system. This is not true in the field of non-linear dynamics or, in studying the solar system

• CPU time is totally dominated by the calculation of forces.

• Memory limits are not too important.

• Energy conservation is important; roughly equivalent to time-reversal invariance.: allow 0.01kT fluctuation in the total energy.


The Verlet Algorithm

The nearly universal choice for an integrator is the Verlet (leapfrog) algorithm

r(t+h) = r(t) + v(t) h + 1/2 a(t) h2 + b(t) h3 + O(h4) Taylor expand

r(t-h) = r(t) - v(t) h + 1/2 a(t) h2 - b(t) h3 + O(h4) Reverse time

r(t+h) = 2 r(t) - r(t-h) + a(t) h2 + O(h4) Add

v(t) = (r(t+h) - r(t-h))/(2h) + O(h2) estimate velocities

Time reversal invariance is built in the energy does not drift.

2 3 4 51 6 7 9 10 11 12 138


How to set the time step• Adjust to get energy conservation to 1% of kinetic energy.

• Even if errors are large, you are close to the exact trajectory of a nearby physical system with a different potential.

• Since we don’t really know the potential surface that accurately, this is satisfactory.

• Leapfrog algorithm has a problem with round-off error.

• Use the equivalent velocity Verlet instead:

r(t+h) = r(t) +h [ v(t) +(h/2) a(t)]

v(t+h/2) = v(t)+(h/2) a(t)

v(t+h)=v(t+h/2) + (h/2) a(t+h)


Higher Order Methods?

• Higher order does not mean better

• Problem is that potentials are not analytic

• Systematic errors

• study from Berendsen 86

Statistical error for fixed CPU time.


Spatial Boundary Conditions

Important because spatial scales are limited. What can we choose?

• No boundaries; e.g. droplet, protein in vacuum. If droplet has 1 million atoms and surface layer is 5 atoms thick 25% of atoms are on the surface.

• Periodic Boundaries: most popular choice because there are no surfaces (see next slide) but there can still be problems.

• Simulations on a sphere

• External potentials

• Mixed boundaries (e.g. infinite in z, periodic in x and y)


Periodic Boundary Conditions

Is the system periodic or just an infinite array of images?

How do you calculate distances in a periodic system?


Minimum Image Convention: take the closest distance:|r|M = min ( r+nL)

• Potential is cutoff so that V(r)=0 for r>L/2 since force needs to be continuous. How about the derivative?

• Image potential

VI = v(ri-rj+nL)

• For long range potential this leads to the Ewald image potential. You need a back ground and convergence method (more later)

Periodic distances

x

-L -L/2 0 L/2 L


Complexity of Force Calculations

• Complexity is defined as how a computer algorithm scales with the number of degrees of freedom (particles)

• Number of terms in pair potential is N(N-1)/2 O(N2)

• For short range potential you can use neighbor tables to reduce it to O(N)– (Verlet) neighbor list for systems that move slowly

– bin sort list (map system onto a mesh and find neighbors from the mesh table)

• Long range potentials with Ewald sums are O(N3/2) but Fast Multipole Algorithms are O(N) for very large N.


! Loop over all pairs of atoms.

do i=2,natoms

do j=1,i-1

!Compute distance between i and j.

r2 = 0

do k=1,ndim

dx(k) = r(k,i) - r(k,j)

!Periodic boundary conditions.

if(dx(k).gt. ell2(k)) dx(k) = dx(k)-ell(k)

if(dx(k).lt.-ell2(k)) dx(k) = dx(k)+ell(k)

r2 = r2 + dx(k)*dx(k)

enddo

!Only compute for pairs inside radial cutoff.

if(r2.lt.rcut2) then

r2i=sigma2/r2

r6i=r2i*r2i*r2i

!Shifted Lennard-Jones potential.

pot = pot+eps4*ri6*(ri6-1)- potcut

!Radial force.

rforce = eps24*r6i*r2i*(2*r6i-1)

do k = 1 , ndim

force(k,i)=force(k,i) + rforce*dx(k)

force(k,j)=force(k,j) - rforce*dx(k)

enddo

endif

enddo

enddo

LJ Force Computation


Constant Temperature MD• Problem in MD is how to control the temperature. (BC in

time.)• How to start the system? (sample velocities from a

Gaussian distribution) If we start from a perfect lattice as the system becomes disordered it will suck up the kinetic energy and cool down. (v.v for starting from a gas)

• QUENCH method. Run for a while, compute kinetic energy, then rescale the momentum to correct temperature, repeat as needed.

• Nose-Hoover Thermostat controls the temperature automatically by coupling the microcanonical system to a heat bath

• Methods have non-physical dynamics since they do not respect locality of interactions. Such effects are O(1/N)


Quench method

• Run for a while, compute kinetic energy, then rescale the momentum to correct temperature, repeat as needed.

• Control is at best O(1/N)

2

'

3( 1)

i iI

i iI

i

Tv v

T

m vT

N


Nose-Hoover thermostat• MD in canonical distribution (TVN)

• Introduce a friction force (t)

T Reservoir

SYSTEM

p(t))(t)F(q,dt

dpt

Dynamics of friction coefficient to get canonical ensemble.

Tk2

3Nmv

dt

Qdb

221

Feedback restores makes kinetic energy=temperature

Q= “heat bath mass”. Large Q is weak coupling


Effect of thermostat

System temperature fluctuates but how quickly?

Q=1

Q=100DIMENSION 3TYPE argon 256 48. POTENTIAL argon argon 1 1. 1. 2.5DENSITY 1.05TEMPERATURE 1.15TABLE_LENGTH 10000LATTICE 4 4 4 4SEED 10WRITE_SCALARS 25NOSE 100.RUN MD 2200 .05


• Thermostats are needed in non-equilibrium situations where there might be a flux of energy in or out of the system.

• It is time reversable, deterministic and goes to the canonical distribution but:

• How natural is the thermostat?– Interactions are non-local. They propagate instantaneously

– Interaction with a single heat bath variable-dynamics can be strange. Be careful to adjust the “mass”

REFERENCES1. S. Nose, J. Chem. Phys. 81, 511 (1984); Mol. Phys. 52, 255 (1984).

2. W. Hoover, Phys. Rev. A31, 1695 (1985).


Constant Pressure• To generalize MD, follow similar

procedure as for the thermostat for constant pressure. The size of the box is coupled to the internal pressure

TPN•Volume is coupled to virial pressure

•Unit cell shape can also change.

,2P1

3d

i j dri j

K r


Parrinello-Rahman simulation•500 KCl ions at 300K

•First P=0

•Then P=44kB

•System spontaneously changes from rocksalt to CsCl structure


• Can “automatically” find new crystal structures

• Nice feature is that the boundaries are flexible

• But one is not guaranteed to get out of local minimum

• One can get the wrong answer. Careful free energy calculations are needed to establish stable structure.

• All such methods have non-physical dynamics since they do not respect locality of interactions.

• Non-physical effects are O(1/N)

REFERENCES

1. H. C. Andersen, J. Chem. Phys. 72, 2384 (1980).

2. M. Parrinello and A. Rahman, J. Appl. Phys. 52, 7158 (1981).


Constraints in MD• Some problems require constraints:

– fixed bond length (water molecule). We would like to get rid of high frequency motion (not interesting)

– In LDA-MD we need to keep wavefunctions orthogonal.

– Putting constraints in “by hand” is difficult (for example, working in polar coordinates)

• WARNING: constraints change the ensemble.

• SHAKE algorithm lets dynamics move forward without constraint; then forces it back to satisfy the constraint.

• Let equation for constraint be: (R) =0

• Bond constraint is (R) =|r i -r j|2 - a2


•Add Lagrange multiplier (t) Equation of motion is :m a(t) = F - (t) (R,t)

•Using leapfrog method:R(t+h) =2R(t)-R(t-h) +h2 F(t)/m - h2(t) (R,t)•What should we use for (t)? • Newton’s method so that (R(t+h))=0.

d

-'

•Iterate until convergence.

•With many constraints: cycle through constraint equations.


Brownian dynamics

• Put a system in contact with a heat bath

• Will discuss how to do this later.

• Leads to discontinuous velocities.

• Not necessarily a bad thing, but requires some physical insight into how the bath interacts with the system in question.

• For example, this is appropriate for a large molecule (protein or colloid) in contact with a solvent. Other heat baths in nature are given by phonons and photons,…


Monitoring the simulation

• Static properties: pressure, specific heat etc.

• Density

• Pair correlation in real space and fourier space.

• Order parameters: How to tell a liquid from a solid


Thermodynamic properties

• Internal energy=kinetic energy + potential energy

• Kinetic energy is kT/2 per momentum

• Specific heat = mean squared fluctuation in energy

• pressure can be computed from the virial theorem.

• compressibility, bulk modulus, sound speed

• But we have problems for the basic quantities of entropy and free energy since they are not ratios with respect to the Boltzmann distribution. We will discuss this later.


Microscopic Density(r) = < i (r-r i) >

Or you can take its Fourier Transform:

k = < i exp(ikri) >(This is a good way to smooth the density.)

• A solid has broken symmetry (order parameter). The density is not constant.• At a liquid-gas transition the density is also inhomgeneous.• In periodic boundary conditions the k-vectors are on a grid: k=2/L

(nx,ny,nz) Long wave length modes are absent.

• In a solid Lindemann’s ratio gives a rough idea of melting:

u2= <(ri-zi)2>/d2


Order parameters

• A system has certain symmetries: translation invariance.

• At high temperatures one expect the system to have those same symmetries at the microscopic scale. (e.g. a gas)

• BUT as the system cools those symmetries are broken. (a gas condenses).

• At a liquid gas-transition the density is no longer fixed: droplets form. The density is the order parameter.

• At a liquid-solid transition, both rotational symmetry and translational symmetry are broken

• The best way to monitor the transition is to look for the dynamics of the order parameter.


Electron Density during exchange2d Wigner crystal (quantum)


Snapshots of densities

Liquid or crystal or glass? Blue spots are defects


Density distribution within a helium dropletDuring addition of molecule, it travels from the surface to the interior.

Red is high density, blue low density of helium


Pair Correlation Function, g(r)Primary quantity in a liquid is the probability distribution of pairs of

particles. Given a particle at the origin what is the density of surrounding particles

g(r) = < i<j (ri-rj-r)> (2 /N2)

Density-density correlation

Related to thermodynamic properties.

3NV ( ) d rv(r)g(r)

2iji j

v r


g(r) in liquid and solid helium• First peak is at inter-particle

spacing. (shell around the particle)

• goes out to r<L/2 in periodic boundary conditions.


(The static) Structure Factor S(K)• The Fourier transform of the pair correlation function is

the structure factor

S(k) = <|k|2>/N (1)

S(k) = 1 + dr exp(ikr) (g(r)-1) (2)

• problem with (2) is to extend g(r) to infinity

• This is what is measured in neutron and X-Ray scattering experiments.

• Can provide a direct test of the assumed potential.

• Used to see the state of a system:

liquid, solid, glass, gas? (much better than g(r) )

• Order parameter in solid is G


• In a perfect lattice S(k) will be non-zero only on the reciprocal lattice vectors G: S(G) = N

• At non-zero temperature (or for a quantum system) this structure factor is reduced by the Debye-Waller factor

S(G) = 1+ (N-1)exp(-G2u2/3)• To tell a liquid from a crystal we see how S(G) scales as the

system is enlarged. In a solid, S(k) will have peaks that scale with the number of atoms.

• The compressibility is given by:• We can use this is detect the liquid-gas transition since the

compressibility should diverge as k approaches 0. (order parameter is density)

) TS(0)/(kBT

May 2001 D. Ceperley Simulation Methods 55Crystal liquid


Here is a snapshot of a binary mixture. What correlation function would be important?


• In a perfect lattice S(k) will be non-zero only on the reciprocal lattice vectors: S(G) = N

• At non-zero temperature (or for a quantum system) this structure factor is reduced by the Debye-Waller factor

S(G) = 1+ (N-1)exp(-G2u2/3)• To tell a liquid from a crystal we see how S(G) scales as the

system is enlarged. In a solid, S(k) will have peaks that scale with the number of atoms.

• The compressibility is given by:

We can use this is detect the liquid gas transition since the compressibility should diverge. (order parameter is density)

) TS(0)/(kBT


Dynamical Properties• Fluctuation-Dissipation theorem:

Here A e-iwt is a perturbation and (w) e-iwt is the response of B. We calculate the average on the lhs in equilibrium (no external perturbation).

• Fluctuations we see in equilibrium are equivalent to how a non-equilibrium system approaches equilibrium. (Onsager regression hypothesis)

• Density-Density response function is S(k,w) can be measured by scattering and is sensitive to collective motions.

dt

dA(0)B(t)dte)(

0

it


•Diffusion constant is defined by Fick’s law and controls how systems mix

•Microscopically we calculate:

•Alder-Wainwright discovered long-time tails on the velocity autocorrelation function so that the diffusion constant does not exist in 2D

Diffusion Constant

)t,r(Ddt

d 2

(t)(0) dt

)t6/((t))-(0)(D

031

2

vv

rr


Mixture simulation with CLAMPS

Initial condition Later


Transport Coefficients

• Diffusion: Particle flux

• Viscosity: Stress tensor

• Heat transport: energy current

• Electrical Conductivity: electrical current

These can also be evaluated with non-equilibrium simulations use thermostats to control.

• Impose a shear flow

• Impose a heat flow


CLAMPS input file

TYPE argon 864 48.

POTENTIAL argon argon 1 1. 1. 2.5

DENSITY 0.35

TEMPERATURE 1.418

LATTICE

SEED 10

WRITE_SCALARS 10

WRITE_COORD 20

RUN MD 300 .032 Executes 300 dynamics steps

Setup system

Output intervals


Monte Carlo:Lecture 3

• What is Monte Carlo?

• Theory of Markov processes

• Detailed balance and transition rules.

• Grand Canonical and polymer Reptation

• Can Monte Carlo tell us about real dynamics?

• Why Monte Carlo instead of MD?


Monte Carlo

• Invented in Los Alamos in 1944 and named after the famous casino.

• A way of doing integrals by using random numbers:

i i

i

N )R(P

)R(fN1

)R(P

)R(f

)R(P

)R(f)R(dRP)R(dRfI

lim

P(R)=sampling function

Convergence guaranteed by the Central Limit Theorem

•If P(R)f(R) then variance0

•faster way of doing integrals for dimensions > 4


Theory of Markov processes

• Markov process is a random walk through phase space:Markov process is a random walk through phase space:

s1s2 s3 s4 …

• If it is ergodic, then it will converge to a unique stationary distribution (only one eigenvalue)

• Detailed balance: (s) P(s s’) = (s’)P (s’ s ).

Rate balance from s to s’.

• We achieve detailed balance by rejecting moves. Acceptance probability is:

)s()'ss(T

)'s()s's(T,1min


““Classic” Metropolis methodClassic” Metropolis method

Metropolis Rosenbluth Teller (1953) method:

• Move from s to s’ with probability T(ss’)= constant

• Accept with move with probability:

a(ss’)= min [ 1 , exp ( - (E(s’)-E(s))/kBT ) ]

• Repeat many times

Given ergodicity, the distribution of s will be the canonical

distribution: (s) = exp(-E(s)/kBT)/Z


MONTE CARLO CODE

call initstate(sold)

vold = action(sold)

LOOP{

callsample(sold,snew,pnew,1)

vnew = action(snew)

call sample(snew,sold,pold,0)

a=exp(-vnew+vold)*pold/pnew if(a.gt.sprng()) {

sold=snew

vold=vnew

naccept=naccept+1}

call averages(sold) }

Initialize the state

Sample snew

Trial action

Find prob. of going backward

Acceptance prob.

Accept the move

Collect statistics


How to sample

Snew = Sold + (sprng - 0.5)

Uniform distribution in a cube

•It is more efficient to move one particle at a time because only the energy of that particle comes in and the movement and acceptance ratio will be larger.

' exp ' exp 'i j i jj i

A s s V s V s v r r v r r


Monte Carlo Rules

• Optimize the efficiency= error2 (computer time) with respect to any sampling parameters.

• Measure acceptance ratio. Set to roughly1/2 by varying the “step size”

• Accepted and rejected states count the same!

• Exact: no time step error, no ergodic problems in principle but no dynamics.


Optimizing the moves

• Any transition rule is allowed as long as you can go anywhere in phase space with a finite number of them. (ergodic)

• Try to find a T(s s’) (s’). If you can the acceptance ratio will be 1.

• We can use the forces to push the walk in the right direction. Taylor expand about the current point.

V(r)=V(r0)-F(r)(r-ro)

• Set T(s s’) exp[ -(V(r0)- F(r0)(r-ro))]

• Leads to Force-Bias Monte Carlo

• Related to Brownian motion (Smoluchowski Eq.)


Brownian DynamicsConsider a big molecule in a solvent. In the high viscosity

limit the “master equation” is:

Enforce detailed balance by rejections! (hybrid method)

Dh2)t(0)t(

)t())t(R(DFh)t(R)ht(R

)]t,R()R(F[D)t,R(Dt

)t,R(

2

2

Dh2

))R(DhFR'R(expc)'RR(T

2

Also the equation for Diffusion Quantum Monte Carlo!


Other Ensembles in MC

• Monte Carlo can handle discrete and continuous variables at the same time.

• This allows us to treat other ensembles. For example consider the (, V, T) ensemble. The number of particles is variable.

• We consider moves that change the number of particles by adding or subtracting 1 and accepting or rejecting the move.


Free Energy Computation

V(R)/kT/kTFF/kT edRee 0

•Free energy ( and the entropy) is more difficult since ensemble average are of ratios only.

•Special methods are needed: e.g. thermodynamic integration.

))t(E)t(E(dt

)T(F)T(F 0

0

0

P

T

Integration path

•Find shortest path to known state.


Polymer Simulations

• Polymers move very slowly because of entanglement.

• A good algorithm is “reptation” Cut off one end and grow onto the other end.

• Acceptance probability is change in potential

• Simple moves go quickly through polymer space.

• Completely unphysical dynamics or is it? This may be how entangled polymers actually move.


Continuum of Methods

Path Integral Monte Carlo

Ab initio Molecular Dynamics

semi-empirical Molecular Dynamics

Langevin Equation

Brownian Dynamics

Metropolis Monte Carlo

Kinetic Monte Carlo

The general procedure is to average out fast degrees of freedom.


Monte Carlo Dynamics• MC introduced as a way of sampling configuration space

so that MC dynamics is purely fictitious.

• Brownian dynamics and reptation are a model dynamics.

• If we satisfy certain conservation laws in the dynamics (e.g. locality of moves) then the dynamics is a possible model with “local thermodynamic equilibrium” .

• For Ising model we speak of Kawasaki Dynamics- this respects the locality of spin.

• MC dynamics is useful because it is so much faster than MD.

• MC dynamics neglects long time correlations which can arise from hydrodynamical effects.


Comparison between MC and MD Which is better?

• MD can compute dynamics. MC has a kinetics under user control, but dynamics is not necessarily physical. MC dynamics is useful for studying long-term diffusive process.

• MC is simpler: no forces, no time step errors and a direct simulation of the canonical ensemble.

• In MD you can only work on how to make the CPUtime/physical time faster. In MC you can invent better transition rules and ergodicity is less of a problem. MD is sometimes very effective in highly constrained systems.

• MC is more general, it can handle discrete degrees of freedom (e. g. spin models, quantum systems), grand canonical ensemble...

So you need both! The best is to have both in the same code so you can use MC to warm up the dynamics.


“An intelligent being who, at a given moment, knows all the forces that cause nature to move and the positions of the objects that it is made from, if also it is powerful enough to analyze this data, would have described in the same formula the movements of the largest bodies of the universe and those of the lightest atoms. Although scientific research steadily approaches the abilities of this intelligent being, complete prediction will always remain infinitely far away.”

Laplace, 1820

Although detailed predictions may be impossible, is it possible to exactly calculate the properties of materials?

may 2001d. ceperley simulation methods1 simulation techniques: outline lecture 1:...

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