
Computational Nanoscience, Homework Assignment 1: Averages and Statistical Uncertainty
30 Jan 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
The purpose of this assignment is to explore statistical errors and data correlation.
This assignment is to be completed following lectures 1 and 2 using the "Average" program in the Berkeley Computational Nanoscience Toolkit.University of California, Berkeley

Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a LennardJones Liquid
14 Feb 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
The purpose of this assignment is to perform a full molecular dynamics simulation based on the Verlet algorithm to calculate various properties of a simple liquid, modeled as an ensemble of identical classical particles interacting via the LennardJones potential.
This assignment is to be completed following lectures 3 and 4 using the "LennardJones Molecular Dynamics" program in the Berkeley Computational Nanoscience Toolkit.
University of California, Berkeley

Computational Nanoscience, Homework Assignment 3: Molecular Dynamics Simulation of Carbon Nanotubes
14 Feb 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
The purpose of this assignment is to perform molecular dynamics simulations to calculate various properties of carbon nanotubes using LAMMPS and Tersoff potentials.
This assignment is to be completed following lectures 5 and 6 using the "LAMMPS" program in the Berkeley Computational Nanoscience Toolkit.University of California, Berkeley

Computational Nanoscience, Homework Assignment 4: HardSphere Monte Carlo and Ising Model
05 Mar 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this assignment, you will explore the use of Monte Carlo techniques to look at (1) hardsphere systems and (2) Ising model of the ferromagneticparamagnetic phase transition in twodimensions. This assignment is to be completed following lecture 12 and using the "Hard Sphere Monte Carlo" and the "Ising Model" program in the Berkeley Computational Nanoscience Toolkit.University of California, Berkeley

Computational Nanoscience, Lecture 10: Brief Review, Kinetic Monte Carlo, and Random Numbers
25 Feb 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
We conclude our discussion of Monte Carlo methods with a brief review of the concepts covered in the three previous lectures. Then, the Kinetic Monte Carlo method is introduced, including discussions of Transition State Theory and basic KMC algorithms. A simulation of vacancymediated diffusion is provided as an example of KMC. Finally, a brief primer on random number generation is presented.University of California, Berkeley