Find information on common issues.

Ask questions and find answers from other users.

Suggest a new site feature or improvement.

Check on status of your tickets.

Computational Nanoscience, Lecture 1: Introduction to Computational Nanoscience

5.0 out of 5 stars

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"...

Computational Nanoscience, Lecture 7: Monte Carlo Simulation Part I

15 Feb 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin

The purpose of this lecture is to introduce Monte Carlo methods as a form of stochastic simulation. Some introductory examples of Monte Carlo methods are given, and a basic introduction to relevant concepts in statistical mechanics is presented. Students will be introduced to the Metropolis...

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

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...

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

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...

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

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...

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...

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 problem-solving 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...

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...

Computational Nanoscience, Homework Assignment 1: Averages and Statistical Uncertainty

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