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++ and fortran compiler installed.

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

Class Time: ARC building (108), 3:20-4:40pm on Monday and Wednesday

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
  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)

Prerequisite
  1. Set up the environment on your computer to be able to code in Python or C++. The instructions from 509 can help.
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. Parallel programming with MPI
  5. Exact diagonalization
  6. Quantum Monte Carlo methods
  7. Continuous Time Quantum Monte Carlo method
  8. Density functional theory
  9. Dynamical Mean Field Theory for model Hamiltonians
  10. Local Density Approximation + Dynamical Mean Field Theory (LDA+DMFT)

Optional:
  1. Molecular dynamics simulation
Literature:

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