Path Integral Quantum Monte Carlo Consider a harmonic oscillator potential a classical particle moves back and forth periodically in such a potential x(t)=

Slides:



Advertisements
Similar presentations
Time averages and ensemble averages
Advertisements

Quantum Harmonic Oscillator
Statistical mechanics
Electrical and Thermal Conductivity
Monte Carlo Methods and Statistical Physics
The Quantum Mechanics of Simple Systems
Chapter (6) Introduction to Quantum Mechanics.  is a single valued function, continuous, and finite every where.
Chapter 4 Free and Confined Electrons Lecture given by Qiliang Li Dept. of Electrical and Computer Engineering George Mason University ECE 685 Nanoelectronics.
1 Quantum Monte Carlo Methods Jian-Sheng Wang Dept of Computational Science, National University of Singapore.
Wavefunction Quantum mechanics acknowledges the wave-particle duality of matter by supposing that, rather than traveling along a definite path, a particle.
MAE 552 – Heuristic Optimization Lecture 6 February 6, 2002.
Given the Uncertainty Principle, how do you write an equation of motion for a particle? First, remember that a particle is only a particle sort of, and.
PHYS 3313 – Section 001 Lecture #17
Advanced methods of molecular dynamics Monte Carlo methods
Ch 9 pages Lecture 18 – Quantization of energy.
Ch 23 pages Lecture 15 – Molecular interactions.
Monte Carlo Methods: Basics
Introduction to (Statistical) Thermodynamics
Monte-Carlo simulations of the structure of complex liquids with various interaction potentials Alja ž Godec Advisers: prof. dr. Janko Jamnik and doc.
Molecular Information Content
The Helmholtz free energyplays an important role for systems where T, U and V are fixed - F is minimum in equilibrium, when U,V and T are fixed! by using:
A PPLIED M ECHANICS Lecture 02 Slovak University of Technology Faculty of Material Science and Technology in Trnava.
Basic Monte Carlo (chapter 3) Algorithm Detailed Balance Other points.
1 CE 530 Molecular Simulation Lecture 6 David A. Kofke Department of Chemical Engineering SUNY Buffalo
Ch 9 pages Lecture 22 – Harmonic oscillator.
For a new configuration of the same volume V and number of molecules N, displace a randomly selected atom to a point chosen with uniform probability inside.
The Ideal Monatomic Gas. Canonical ensemble: N, V, T 2.
Monte Carlo Methods So far we have discussed Monte Carlo methods based on a uniform distribution of random numbers on the interval [0,1] p(x) = 1 0  x.
Quantization via Fractional Revivals Quantum Optics II Cozumel, December, 2004 Carlos Stroud, University of Rochester Collaborators:
Geometrical Optics LL2 Section 53. Local propagation vector is perpendicular to wave surface Looks like a plane wave if amplitude and direction are ~constant.
مدرس المادة الدكتور :…………………………
Burkhard Militzer, Carnegie Institution of Washington: “Path Integral Monte Carlo”, 2007 Burkhard Militzer Geophysical Laboratory Carnegie Institution.
Ch 2. The Schrödinger Equation (S.E)
Time-dependent Schrodinger Equation Numerical solution of the time-independent equation is straightforward constant energy solutions do not require us.
NCN nanoHUB.org Wagner The basics of quantum Monte Carlo Lucas K. Wagner Computational Nanosciences Group University of California, Berkeley In collaboration.
Molecular Modelling - Lecture 2 Techniques for Conformational Sampling Uses CHARMM force field Written in C++
Partition functions of ideal gases. We showed that if the # of available quantum states is >> N The condition is valid when Examples gases at low densities.
Light is an electromagnetic wave EM wave- a form of energy that exhibits wavelike behavior as it travels through space All the forms of EM radiation form.
7. Metropolis Algorithm. Markov Chain and Monte Carlo Markov chain theory describes a particularly simple type of stochastic processes. Given a transition.
Interacting Molecules in a Dense Fluid
Monte Carlo in different ensembles Chapter 5
Monatomic Crystals.
Javier Junquera Importance sampling Monte Carlo. Cambridge University Press, Cambridge, 2002 ISBN Bibliography.
Vibrational Motion Harmonic motion occurs when a particle experiences a restoring force that is proportional to its displacement. F=-kx Where k is the.
Physics Lecture 11 3/2/ Andrew Brandt Monday March 2, 2009 Dr. Andrew Brandt 1.Quantum Mechanics 2.Schrodinger’s Equation 3.Wave Function.
LECTURE 17 THE PARTICLE IN A BOX PHYSICS 420 SPRING 2006 Dennis Papadopoulos.
Statistical Mechanics and Multi-Scale Simulation Methods ChBE
Basic Monte Carlo (chapter 3) Algorithm Detailed Balance Other points non-Boltzmann sampling.
Lecture 14: Advanced Conformational Sampling Dr. Ronald M. Levy Statistical Thermodynamics.
Computational Physics (Lecture 11) PHY4061. Variation quantum Monte Carlo the approximate solution of the Hamiltonian Time Independent many-body Schrodinger’s.
Nanoelectronics Chapter 4 Free and Confined Electrons
Review for Exam 2 The Schrodinger Eqn.
Monte Carlo Simulation of the Ising Model Consider a system of N classical spins which can be either up or down. The total.
Introduction to Quantum Monte Carlo Methods 2! Claudio Attaccalite.
Quantum One.
The Quantum Mechanical Picture of the Atom
Schrodinger wave equation
UNIT 1 Quantum Mechanics.
Quantum Mechanics.
Quantum Mechanics.
Concept test 15.1 Suppose at time
CHAPTER 5 The Schrodinger Eqn.
 Heisenberg’s Matrix Mechanics Schrödinger’s Wave Mechanics
CHAPTER 5 The Schrodinger Eqn.
Concept test 15.1 Suppose at time
FERMI-DIRAC DISTRIBUTION.
Atomic Orbitals.
More Quantum Mechanics
Chapter 5 - Phonons II: Quantum Mechanics of Lattice Vibrations
Source: D. Griffiths, Introduction to Quantum Mechanics (Prentice Hall, 2004) R. Scherrer, Quantum Mechanics An Accessible Introduction (Pearson Int’l.
Presentation transcript:

Path Integral Quantum Monte Carlo Consider a harmonic oscillator potential a classical particle moves back and forth periodically in such a potential x(t)= A cos(  t) the quantum wave function can be thought of as a fluctuation about the classical trajectory

Feynman Path Integral The motion of a quantum wave function is determined by the Schrodinger equation we can formulate a Huygen’s wavelet principle for the wave function of a free particle as follows: each point on the wavefront emits a spherical wavelet that propagates forward in space and time

Feynman Paths The probability amplitude for the particle to be at x b is the sum over all paths through spacetime originating at x a at time t a

Principal Of Least Action Classical mechanics can be formulated using Newton’s equations of motion or in terms of the principal of least action given two points in space-time, a classical particle chooses the path that minimizes the action Fermat

Path Integral L is the Lagrangian L=T-V similarly, quantum mechanics can be formulated in terms of the Schrodinger equation or in terms of the action the real time propagator can be expresssed as

Propagator The sum is over all paths between (x 0,0) and (x,t) and not just the path that minimizes the classical action the presence of the factor i leads to interference effects the propagator G(x,x 0,t) is interpreted as the probability amplitude for a particle to be at x at time t given it was at x 0 at time zero

Path Integral We can express G as Using imaginary time =it/ 

Path Integrals Consider the ground state as  hence we need to compute G and hence S to obtain properties of the ground state

Lagrangian Using imaginary time =it the Lagrangian for a particle of unit mass is divide the imaginary time interval into N equal steps of size  and write E as

Action Where j = j  and x j is the displacement at time j

Propagator The propagator can be expressed as

Path Integrals This is a multidimensional integral the sequence x 0,x 1,…,x N is a possible path the integral is a sum over all paths for the ground state, we want G(x 0,x 0,N  ) and so we choose x N = x 0 we can relabel the x’s and sum j from 1 to N

Path Integral We have converted a quantum mechanical problem for a single particle into a statistical mechanical problem for N “atoms” on a ring connected by nearest neighbour springs with spring constant 1/(  ) 2

Thermodynamics This expression is similar to a partition function Z in statistical mechanics the probability factor e -  E in statistical mechanics is the analogue of e - E in quantum mechanics =N  plays the role of inverse temperature  =1/kT

Simulation We can use the Metropolis algorithm to simulate the motion of N “atoms” on a ring these are not real particles but are effective particles in our analysis possible algorithm: 1. Choose N and  such that N  >>1 ( low T) also choose  ( the maximum trial change in the displacement of an atom) and mcs (the number of steps)

Algorithm 2. Choose an initial configuration for the displacements x j which is close to the approximate shape of the ground state probability amplitude 3. Choose an atom j at random and a trial displacement x trial ->x j +(2r-1)  where r is a random number on [0,1] 4. Compute the change  E in the energy

Algorithm If  E <0, accept the change otherwise compute p=e -   E and a random number r in [0,1] if r < p then accept the move if r > p reject the move

Algorithm 4. Update the probability density P(x). This probability density records how often a particular value of x is visited Let P(x=x j ) => P(x=x j )+1 where x was position chosen in step 3 (either old or new) 5. Repeat steps 3 and 4 until a sufficient number of Monte Carlo steps have been performed qmc1

Excited States To get the ground state we took the limit  this corresponds to T=0 in the analogous statistical mechanics problem for finite T, excited states also contribute to the path integrals the paths through spacetime fluctuate about the classical trajectory this is a consequence of the Metropolis algorithm occasionally going up hill in its search for a new path