Advanced Computational Physics, Course 681 - Special Topics in Condensed Matter Physics

Overview


Perturbation Theory

Random Numbers

Monte Carlo

Quantum Monte Carlo

Continuous Time QMC

Dynamical Mean Field

LDA+DMFT


Density functional theory

Molecular Dynamics



    
Left: Simulation of a bacteria growth by DLA method, Middle: Molecular dynamics simulation of a small system of atoms Right: Band structure of a heavy fermion material.

This course is a continuation of Computational Physics course (509). It introduces andvanced concepts and algorithms in Computational Condensed Matter Physics and brings students to the active research in Computational Condensed Matter area.

Lectures will be given in "hands on" style only, and students should bring their own laptops to follow the lectures. Latops should run python (including numpy, scipy, weave, matplotlib) and should have C++ (preferably gnu ) and fortran90 (preferably intel) compiler installed.

This course requires familiarity with some basics of programming languages such as Python and C++. It is designed for the student who wishes to broaden his/her knowledge of applications and develop techniques.

Class Time: ARC building (212), 10:20-11:40 on Monday and 12:00-1:20pm on Monday

Special note: First lecture will be given on Friday Jan 22 at 10:20am in ARC 204.

Instructor: Kristjan Haule
Office: Serin E267
email: haule@physics.rutgers.edu
Phone: 445 5500, ext: 3881
Office hours: Monday 4 pm

 

If you are not yet familiar with Python, or you just want to refresh your memory, check out some of these links:

  1. Learn Python in 10 minutes
  2. How to Think Like a Computer Scientist: Learning with Python
  3. Python for beginners
  4. Dive Into Python
  5. Code Like a Pythonista: Idiomatic Python
  6. Python documentation
  7. Python regular expressions
  8. Weave (to speed up the Python code)

Preliminary Course Outline and Tentative List of Topics include
  1. Perturbation theory at low orders
  2. Random numbers and multidimensional integration
  3. Monte Carlo methods and Simulated Annealing
  4. Quantum Monte Carlo methods
  5. Continuous Time Quantum Monte Carlo method
  6. Dynamical Mean Field Theory for model Hamiltonians
  7. Local Density Approximation + Dynamical Mean Field Theory (LDA+DMFT) in action

Optional:
  1. Parallel programming with MPI
  2. Density functional theory
  3. Molecular dynamics simulation
Literature:

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