
Scaffolding Simulations in a Rate Processes of Materials Course
16 Aug 2018  Contributor(s):: Susan P Gentry
This learning resource describes a set of programming assignments that are used in a Rate Processes of Materials course. The assignments are designed around the pedagogical principle of scaffolding, in which students are given initial support structures that are gradually removed. The...

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, and...

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, PopQuiz
15 May 2008   Contributor(s):: Elif Ertekin, Jeffrey C Grossman
This quiz summarizes the most important concepts which have covered in class so far related to Molecular Dynamics, Classical Monte Carlo Methods, and Quantum Mechanical Methods.University of California, Berkeley

Computational Nanoscience, PopQuiz Solutions
15 May 2008   Contributor(s):: Elif Ertekin, Jeffrey C Grossman
The solutions to the popquiz are given in this handout.University of California, Berkeley

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...

Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology
16 May 2008   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   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   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 17: TightBinding, and Moving Towards Density Functional Theory
21 Mar 2008   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 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, 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 9: HardSphere Monte Carlo InClass Simulation
19 Feb 2008   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...

Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II
14 Feb 2008   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...

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 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, Lecture 6: Pair Distribution Function and More on Potentials
13 Feb 2008   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...