
Computational Nanoscience for Energy
15 Sep 2009  Teaching Materials  Contributor(s): Jeffrey C Grossman, Alexander S McLeod
Materials for energy conversion and storage can be greatly improved by taking advantage of unique effects that occur at the nanoscale. In many cases, these improvements are due to fundamental microscopic mechanisms that can be understood and predicted by cuttingedge simulation methods. This course …

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 …

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 …

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 …

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 …

Computational Nanoscience, Lecture 11: Phase Transitions and the Ising Model
27 Feb 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this lecture, we present an introduction to simulations of phase transitions in materials. The use of Monte Carlo methods to model phase transitions is described, and the Ising Model is given as an example for modeling the ferromagneticparamagnetic transition. Some of the subtleties of …

Computational Nanoscience, Lecture 12: InClass Simulation of Ising Model
28 Feb 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
This is a two part lecture in which we discuss the spinspin correlation function for the the Ising model, correlation lengths, and critical slowing down. An inclass simulation of the 2D Ising Model is performed using the tool "Berkeley Computational Nanoscience Class Tools". We look at domain …

Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics
30 Apr 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this lecture we introduce the basic concepts that will be needed as we explore simulation approaches that describe the electronic structure of a system.

Computational Nanoscience, Lecture 14: HartreeFock Calculations
30 Apr 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
A description of the HartreeFock method and practical overview of its application. This lecture is to be used in conjunction with the course toolkit, with the HartreeFock simulation module.

Computational Nanoscience, Lecture 15: InClass Simulations: HartreeFock
30 Apr 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
Using a range of examples, we study the effect of basis set on convergence, the HartreeFock accuracy compared to experiment, and explore a little bit of molecular chemistry.

Computational Nanoscience, Lecture 16: More and Less than HartreeFock
30 Apr 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
In the lecture we discuss both techniques for going "beyond" HartreeFock in order to include correlation energy as well as techniques for capturing electronic structure effects while not having to solve the full HartreeFock equations (ie, semiempirical methods). We also very briefly touch upon …

Computational Nanoscience, Lecture 17: TightBinding, and Moving Towards Density Functional Theory
21 Mar 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
The purpose of this lecture is to illustrate the application of the TightBinding method to a simple system and then to introduce the concept of Density Functional Theory. The motivation to mapping from a wavefunction to a densitybased description of atomic systems is provided, and the necessary …

Computational Nanoscience, Lecture 18.5: A Little More, and Lots of Repetition, on Solids
30 Apr 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
Here we go over again some of the basics that one needs to know and understand in order to carry out electronic structure, atomicscale calculations of solids.

Computational Nanoscience, Lecture 18: Density Functional Theory and some Solid Modeling
21 Mar 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
We continue our discussion of Density Functional Theory, and describe the mostoften used approaches to describing the exchangecorrelation in the system (LDA, GGA, and hybrid functionals). We discuss as well the strengths and weaknesses of the LDA and present some examples of its use. Finally, a …

Computational Nanoscience, Lecture 19: Band Structure and Some InClass Simulation: DFT for Solids
30 Apr 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
In this class we briefly review band structures and then spend most of our class on inclass simulations. Here we use the DFT for molecules and solids (Siesta) course toolkit. We cover a variety of solids, optimizing structures, testing kpoint convergence, computing cohesive energies, and …

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" …

Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
15 May 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
This lecture provides and introduction to Quantum Monte Carlo methods. We review the concept of electron correlation and introduce Variational Monte Carlo methods as an approach to going beyond the mean field approximation. We describe briefly the SlaterJastrow expansion of the wavefunction, and …

Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II
15 May 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
This is our second lecture in a series on Quantum Monte Carlo methods. We describe the Diffusion Monte Carlo approach here, in which the approximation to the solution is not restricted by choice of a functional form for the wavefunction. The DMC approach is explained, and the fixed node …

Computational Nanoscience, Lecture 23: Modeling Morphological Evolution
15 May 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this lecture, we present an introduction to modeling the morphological evolution of materials systems. We introduce concepts of coarsening, particlesize distributions, the LifshitzSlyozovWagner model, thin film growth modes (LayerbyLayer, Island growth, and StranskiKrastanov), and …

Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology
16 May 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this lecture, we describe the challenges in simulating water and introduce both explicit and implicit approaches. We also briefly describe protein structure, the Levinthal paradox, and simulations of proteins and protein structure using First Principles approaches and Monte Carlo …

Computational Nanoscience, Lecture 28: WishList, Reactions, and XRays.
16 May 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
After a brief interlude for class feedback on the course content and suggestions for next semester, we turn to modeling chemical reactions. We describe chainofstate methods such as the Nudged Elastic Band for determining energy barriers. The use of empirical, QM/MM methods are described. We …

Computational Nanoscience, Lecture 29: Verification, Validation, and Some Examples
16 May 2008  Teaching Materials  Contributor(s): Jeffrey C Grossman, Elif Ertekin
We conclude our course with a lecture of verification, and validation. We describe what each of these terms means, and provide a few recent examples of nanoscale simulation in terms of these concepts.University of California, Berkeley

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, 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 …