
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 ...

Excellence in Computer Simulation
19 Dec 2007  Workshops  Contributor(s): Mark Lundstrom, Jeffrey B. Neaton, Jeffrey C Grossman
Computational science is frequently labeled as a third branch of science  equal in standing with theory and experiment, and computational engineering is now an essential component of technology development and manufacturing. The successes of computational science and engineering (CSE) over the past twothree decades have been substantial, but at the beginning of a new century, it is useful to reflect on what has been accomplished, on how computational science and engineering are evolving, and ...

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 approach to Monte Carlo simulation. Using Metropolis as an example, these lectures also introduce the comcepts of balance and detailed balance, and what "efficient sampling" means.

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

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 wall formation at low temperature, and the phase transition for the antiferromagnetic and ferromagnetic system.
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 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 computing band structures and density of states.

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 simulating phase transitions are also discussed, including finite size effects and critical slowing down. The concept of linear response is introduced as well.University of California, Berkeley

Computational Nanoscience, Lecture 9: HardSphere Monte Carlo InClass Simulation
19 Feb 2008  Teaching Materials  Contributor(s): Elif Ertekin, Jeffrey C Grossman
In this lecture we carry out simulations inclass, 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 orderdisorder phase transition, which we will explore with Monte Carlo simulations.Nanoscale Science and Engineering C242/Physics C203 University of California, Berkeley

The basics of quantum Monte Carlo
15 Jun 2007  Online Presentations  Contributor(s): Lucas Wagner, Jeffrey C Grossman, Jeffrey B. Neaton
Quantum Monte Carlo is a highly accurate method to approximately solve the Schrodinger equation. I explain quantum Monte Carlo in a way that should be accessible to someone who is somewhat familiar with quantum mechanics. The discussion is mostly conceptual.Lucas Wagner is a postdoctoral researcher in the Computational Nanosciences group at UC Berkeley, under the supervision of Dr. Jeffrey Grossman. One of his PhD projects was the development of a general purpose quantum Monte Carlo program ...

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

The Helios Talks
25 Sep 2007  Series  Contributor(s): Joe Ringgenberg, Jeffrey B. Neaton, Jeffrey C Grossman
The energy problem is one of the most important issues that science and technology has to solve.
The Lawrence Berkeley National Laboratory’s Helios Project concentrates on renewable fuels, such as biofuels, and solar technologies, including a new generation of solar photovoltaic cells and the conversion of electricity into chemical storage to meet future demand.

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 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 short introduction to modeling band structures in solids is presented.University of California, Berkeley

Nano*High: Nanoscience for High School Students
02 Feb 2010  Series  Contributor(s): Alexander S McLeod, Jeffrey B. Neaton, Jeffrey C Grossman
The Materials Sciences Division at the University of California's Lawrence Berkeley National Laboratory invites you and your students to Nano*High, a series of free Saturday morning lectures by UC Berkeley professors and LBNL senior scientists conducting research from nanoscience to molecular medicine, and climate change to astrophysics. 20092010 will be our seventh year of Nano*High. Last year over 900 students and their teachers attended at least one talk. Nano*High talks are aimed at all ...

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, 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 approximation is described as well. We conclude with a few examples demonstrating the application of VMC and DMC to methane and ethane.Lucas K. Wagner
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

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 ingredients to do so (two HohenbergKohn Theorems and the KohnSham formalism) is presented.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, Lecture 8: Monte Carlo Simulation Part II
14 Feb 2008  Teaching Materials  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 statistics, result interpretation, and error analysis is presented as well.University of California, Berkeley.

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 ...

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 show how we can recover the some of the correlation energy using a variational approach to optimizing this form of the wavefunction.Lucas K. Wagner
University of California, Berkeley

Nanoscience at Work: Creating Energy from Sunlight
13 Jun 2007  Online Presentations  Contributor(s): A. Paul Alivisatos
Professor Paul Alivisatos introduces the Helios Project for the 2007 'Science at the Theater' series at Berkeley Repertory Theater in Berkeley, California. He discusses how Helios Project researchers use nanotechnology in the efficient capture of sunlight, and its conversion to electricity to drive economical fuel production processes. The Lawrence Berkeley National Laboratory’s Helios Project concentrates on renewable fuels such as biofuels, solar technologies such as photovoltaic cells, and the conversion of electricity into chemical storage.

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