Tags: computational science/engineering

Teaching Materials (21-31 of 31)

  1. Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation

    19 Feb 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    In this lecture we carry out simulations in-class, with guidance from the instructors. We use the HSMC tool (within the nanoHUB simulation toolkit for this course). The hard sphere system is one of the simplest systems which exhibits an order-disorder phase transition, which we will explore with...

  2. Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II

    14 Feb 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    In this lecture, we continue our discussion of Monte Carlo simulation. Examples from Hard Sphere Monte Carlo simulations based on the Metropolis algorithm and from Grand Canonical Monte Carlo simulations of fullerene growth on spherical surfaces are presented. A discussion of meaningful...

  3. Computational Nanoscience, Homework Assignment 3: Molecular Dynamics Simulation of Carbon Nanotubes

    14 Feb 2008 | | 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...

  4. Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a Lennard-Jones Liquid

    14 Feb 2008 | | 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 Lennard-Jones potential.This assignment is to be...

  5. Computational Nanoscience, Lecture 1: Introduction to Computational Nanoscience

    13 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    In this lecture, we present a historical overview of computational science. We describe modeling and simulation as forms of "theoretical experiments" and "experimental theory". We also discuss nanoscience: "what makes nano nano?", as well as public perceptions of nanoscience and the "grey goo"...

  6. Computational Nanoscience, Lecture 6: Pair Distribution Function and More on Potentials

    13 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    In this lecture we remind ourselves what a pair distribution function is, how to compute it, and why it is so important in simulations. Then, we revisit potentials and go into more detail including examples of typical functional forms, relative energy scales, and what to keep in mind when...

  7. Computational Nanoscience, Lecture 5: A Day of In-Class Simulation: MD of Carbon Nanostructures

    13 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    In this lecture we carry out simulations in-class, with guidance from the instructors. We use the LAMMPS tool (within the nanoHUB simulation toolkit for this course). Examples include calculating the energy per atom of different fullerenes and nantubes, computing the Young's modulus of a...

  8. Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing

    13 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    In this lecture, we discuss various methods for finding the ground state structure of a given system by minimizing its energy. Derivative and non-derivative methods are discussed, as well as the importance of the starting guess and how to find or generate good initial structures. We also briefly...

  9. Computational Nanoscience, Lecture 3: Computing Physical Properties

    11 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    In this lecture, we'll cover how to choose initial conditions, and how to compute a number of important physical observables from the MD simulation. For example, temperature, pressure, diffusion coefficient, and pair distribution function will be highlighted. We will also discuss briefly the use...

  10. Computational Nanoscience, Lecture 2: Introduction to Molecular Dynamics

    30 Jan 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    In this lecture, we present and introduction to classical molecular dynamics. Approaches to integrating the equations of motion (Verlet and other) are discussed, along with practical considerations such as choice of timestep. A brief discussion of interatomic potentials (the pair potential and...

  11. Computational Nanoscience, Homework Assignment 1: Averages and Statistical Uncertainty

    30 Jan 2008 | | 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