Computational Physics 2021, Course 509 - Physics Applications of Computers


Introductory lecture material


Basic numerical methods

Methods and Algorithms
Monte Carlo methods
Quantum Monte Carlo methods
Continuous Time Quantum Monte Carlo
Hartree-Fock method
Density functional theory
Molecular Dynamics

Left: Simulation of a bacteria growth by DLA method, Right: Molecular dynamics simulation of a small system of atoms Simulation codes is available to download in lecture material.

This course introduces logarithmic concepts and familiarizes students with the basic conputational tools which are essential for graduate students in computational physics and related areas. In this course, students work toward mastering computational skills, needed to work in classical and quantum physics using the computer. Examples will be drawn from various areas of physics. As the programing language, we will use mostly Python and its scientific library scipy & numpy. To speed up parts of the code, we will also use C++ and fortran90 for short examples, which will be used through the Python interface. This course has no prerequsites except for familiarity with some programming language. It is designed for the student who wishes to broaden his/her knowledge of applications and develop techniques. To follow the course more efficiently, and perform the hands on training, it is desired that students bring their own laptops to the class.


Class Time: Online (you should get Zoom link) 5pm-6:20pm Monday and Wednesday

Instructor: Kristjan Haule
Office: Serin E267
Phone: 445-3881
Office hours: after lecture



Student's survey link
QR code for survey

Codes available at

Youtube video available at: Lecture 1, Lecture 2, Lecture 3, Lecture 4, Lecture 5, Lecture 6, Lecture 7, Lecture 8, Lecture 9, Lecture 10, Lecture 11, Lecture 12, Lecture 13, Lecture 14, Lecture 15, Lecture 16, Lecture 17, Lecture 18, Lecture 19, Lecture 20, Lecture 21, Lecture 22, Lecture 23, Lecture 24, Lecture 25

Preliminary Course Outline and Tentative List of Topics include


    System and Python instalation including libraries and environment, Comparison of modern programing languages, speed up the code with numba, f2py, pybind11, parallelization with openMP, roundoff error & recursion,...
B) Learning high level programming with examples in Python:
    Modern Scientific Computing using Python, SciPy, Numpy, Pylab
Literature for Pythons:
  1. Software carpentry
  2. How to Think Like a Computer Scientist: Learning with Python
  3. Python for beginners
  4. Python documentation
  5. Python regular expressions
C) Computational methods and algorithms:
  1. Random numbers, Monte Carlo methods and Simulated Annealing

  2. Hartree-Fock method
  3. Density functional theory
  4. Quantum Monte Carlo methods
  5. Continuous Time Quantum Monte Carlo method
  6. Molecular dynamics simulation
  1. Computational Physics by J.M. Thijssen
  2. Introduction to Computer Simulation Methods by H. Gould, J Tobocnik and W. Christian
  3. Electronic Structure, Basic Theory and Practical Methods by Richard M. Martin(Very good book for the Density functional part of the course)
  4. An Introduction to Computational Physics by Tao Pang
  5. Computational Physics by Rubin H. Landau and Manuel J. Paez(More elementary but good book)
D) Basic numerical methods:
  1. Numeric integration (source code)
  2. Interpolation, Splines and Fourier transformation (source code)
  3. Differential equations (source code)
  4. Parallel programming with MPI ( source code)
Literature: Numerical Recipes online from

Students with Disabilities