
ABINIT: FirstTime User Guide
09 Jun 2009   Contributor(s):: Benjamin P Haley
This firsttime user guide provides an introduction to using ABINIT on nanoHUB. We include a very brief summary of Density Functional Theory along with a tour of the Rappture interface. We discuss the default simulation (what happens if you don't change any inputs, and just hit "simulate") as...

Are Simulation Tools Developed and Used by Experts Appropriate Experimentation Tools for Educational Contexts?
08 Apr 2010   Contributor(s):: Alejandra J. Magana, Sean Brophy,
Simulations and visualizations can lead to significant improvements in students'conceptual understanding. This increased understanding may be due to the formation of expertlike dynamic mental models. Laboratory simulations have been used in educational contexts forinquiry learning by allowing...

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

Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a LennardJones 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 LennardJones potential.This assignment is to be...

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...

Computational Nanoscience, Homework Assignment 4: HardSphere Monte Carlo and Ising Model
05 Mar 2008   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   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   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   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   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   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   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   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...

Computational Nanoscience, Lecture 18.5: A Little More, and Lots of Repetition, on Solids
30 Apr 2008   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 19: Band Structure and Some InClass Simulation: DFT for Solids
30 Apr 2008   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   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   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,...

Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II
15 May 2008   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   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 26: Life Beyond DFT  Computational Methods for Electron Correlations, Excitations, and Tunneling Transport
16 May 2008   Contributor(s):: Jeffrey B. Neaton
In this lecture, we provide a brief introduction to "beyond DFT" methods for studying excited state properties, optical properties, and transport properties. We discuss how the GW approximation to the selfenergy corrects the quasiparticle excitations energies predicted by KohnSham DFT. For...