
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, Lecture 1: Introduction to Computational Nanoscience
13 Feb 2008  Teaching Materials  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" phenomenon. Finally, we describe the process of setting up a computer experiment: choosing your model, making relevant assumptions, and interpreting your resutls.UC Berkeley

Computational Nanoscience, Lecture 6: Pair Distribution Function and More on Potentials
13 Feb 2008  Teaching Materials  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 developing or using a potential.Nanoscale Science and Engineering C242/Physics C203
University of California, Berkeley

Computational Nanoscience, Lecture 5: A Day of InClass Simulation: MD of Carbon Nanostructures
13 Feb 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this lecture we carry out simulations inclass, 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 nanotube with and without a StoneWales defect, and examining the effects of temperature.Nanoscale Science and Engineering C242/Physics C203
University of California, Berkeley

Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing
13 Feb 2008  Teaching Materials  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 nonderivative methods are discussed, as well as the importance of the starting guess and how to find or generate good initial structures. We also briefly touch on the importance of visualizing your structures and the broad range of file formats for keeping structural data.Nanoscale Science and Engineering C242/Physics C203
University of California, ...

Computational Nanoscience, Lecture 3: Computing Physical Properties
11 Feb 2008  Teaching Materials  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 of periodic boundary conditions and its impact on the potential. This lecture enables students to conduct LennardJones molecular dynamics simulations using the course toolkit for homework ...

Overview of Computational Nanoscience: a UC Berkeley Course
01 Feb 2008  Courses  Contributor(s): Jeffrey C Grossman, Elif Ertekin
This course will provide students with the fundamentals of computational problemsolving techniques that are used to understand and predict properties of nanoscale systems. Emphasis will be placed on how to use simulations effectively, intelligently, and cohesively to predict properties that occur at the nanoscale for real systems. The course is designed to present a broad overview of computational nanoscience and is therefore suitable for both experimental and theoretical researchers.
Specific ...

Computational Nanoscience, Lecture 2: Introduction to Molecular Dynamics
30 Jan 2008  Teaching Materials  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 LennardJones) is provided. Finally, this lecture enables students to understand simulation results by computing physical averages and understanding systematic and statistical errors, error bars, ...

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

MIT Atomic Scale Modeling Toolkit
15 Jan 2008  Tools  Contributor(s): daniel richards, Elif Ertekin, Jeffrey C Grossman, David Strubbe, Justin Riley
Tools for Atomic Scale Modeling