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Computational Nanoscience, Lecture 29: Verification, Validation, and Some Examples

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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 28: Wish-List, Reactions, and X-Rays.

After a brief interlude for class feedback on the course content and suggestions for next semester, we turn to modeling chemical reactions. We describe chain-of-state methods such as the Nudged Elastic Band for determining energy barriers. The use of empirical, QM/MM methods are described. We...

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 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, particle-size distributions, the Lifshitz-Slyozov-Wagner model, thin film growth modes (Layer-by-Layer, Island growth, and Stranski-Krastanov), and...

Computational Nanoscience, Pop-Quiz Solutions

The solutions to the pop-quiz are given in this handout.University of California, Berkeley

Computational Nanoscience, Pop-Quiz

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, 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 20: Quantum Monte Carlo, part I

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 Slater-Jastrow expansion of the wavefunction,...

Computational Nanoscience, Lecture 19: Band Structure and Some In-Class 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 in-class simulations. Here we use the DFT for molecules and solids (Siesta) course toolkit. We cover a variety of solids, optimizing structures, testing k-point convergence, computing cohesive energies, and...

Computational Nanoscience, Lecture 18.5: A Little More, and Lots of Repetition, on Solids

Here we go over again some of the basics that one needs to know and understand in order to carry out electronic structure, atomic-scale calculations of solids.

Computational Nanoscience, Lecture 16: More and Less than Hartree-Fock

In the lecture we discuss both techniques for going "beyond" Hartree-Fock in order to include correlation energy as well as techniques for capturing electronic structure effects while not having to solve the full Hartree-Fock equations (ie, semi-empirical methods). We also very briefly touch...

Computational Nanoscience, Lecture 15: In-Class Simulations: Hartree-Fock

Using a range of examples, we study the effect of basis set on convergence, the Hartree-Fock accuracy compared to experiment, and explore a little bit of molecular chemistry.

Computational Nanoscience, Lecture 14: Hartree-Fock Calculations

A description of the Hartree-Fock method and practical overview of its application. This lecture is to be used in conjunction with the course toolkit, with the Hartree-Fock simulation module.

Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics

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 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 most-often used approaches to describing the exchange-correlation 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,...

Computational Nanoscience, Lecture 17: Tight-Binding, and Moving Towards Density Functional Theory

The purpose of this lecture is to illustrate the application of the Tight-Binding method to a simple system and then to introduce the concept of Density Functional Theory. The motivation to mapping from a wavefunction to a density-based description of atomic systems is provided, and the...

Computational Nanoscience, Homework Assignment 4: Hard-Sphere 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) hard-sphere systems and (2) Ising model of the ferromagnetic-paramagnetic phase transition in two-dimensions. This assignment is to be completed following lecture 12 and using the "Hard Sphere Monte Carlo" and...

Computational Nanoscience, Lecture 12: In-Class 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 spin-spin correlation function for the the Ising model, correlation lengths, and critical slowing down. An in-class simulation of the 2D Ising Model is performed using the tool "Berkeley Computational Nanoscience Class Tools". We look at domain...

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 ferromagnetic-paramagnetic transition. Some of the subtleties of...

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 vacancy-mediated diffusion...

Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation

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

In this lecture we carry out simulations in-class, 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 order-disorder phase transition, which we will explore with...

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...

Computational Nanoscience, Homework Assignment 3: Molecular Dynamics Simulation of Carbon Nanotubes

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 Lennard-Jones Liquid

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 Lennard-Jones potential.This assignment is to be...

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"...