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Computational Nanoscience, Lecture 15: In-Class Simulations: Hartree-Fock
Teaching Materials | 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 Hartree-Fock accuracy compared to experiment, and explore a little bit of molecular chemistry.
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Computational Nanoscience, Lecture 14: Hartree-Fock Calculations
Teaching Materials | 30 Apr 2008 | Contributor(s):: Jeffrey C Grossman, Elif Ertekin
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.
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Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics
Teaching Materials | 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.
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nanoHUB.org: Future Cyberinfrastructure Serving a Community of 60,000 Today
Online Presentations | 23 Apr 2008 | Contributor(s):: George B. Adams III, Gerhard Klimeck, Mark Lundstrom, Michael McLennan
nanoHUB.org provides users with "fingertip access" to over 70 simulation tools for research and education. Users not only launch jobs that are executed on the state-of-the-art computational facilities of Open Science Grid and TeraGrid, but also interactively visualize and analyze the results—all...
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UV/Vis Spectra simulator
Tools | 04 Mar 2008 | Contributor(s):: Baudilio Tejerina
This tool computes molecular electronic spectra.
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Computational Nanoscience, Lecture 12: In-Class Simulation of Ising Model
Teaching Materials | 28 Feb 2008 | 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...
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Computational Nanoscience, Homework Assignment 4: Hard-Sphere Monte Carlo and Ising Model
Teaching Materials | 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) 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...
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Computational Nanoscience, Lecture 10: Brief Review, Kinetic Monte Carlo, and Random Numbers
Teaching Materials | 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 vacancy-mediated diffusion...
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Computational Nanoscience, Lecture 11: Phase Transitions and the Ising Model
Teaching Materials | 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 ferromagnetic-paramagnetic transition. Some of the subtleties of...
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Quantum and Semi-classical Electrostatics Simulation of SOI Trigates
Tools | 19 Feb 2008 | Contributor(s):: Hyung-Seok Hahm, Andres Godoy
Generate quantum/semi-classical electrostatic simulation results for a simple Trigate structure
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Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation
Teaching Materials | 19 Feb 2008 | 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...
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Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II
Teaching Materials | 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...
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Computational Nanoscience, Homework Assignment 3: Molecular Dynamics Simulation of Carbon Nanotubes
Teaching Materials | 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...
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Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a Lennard-Jones Liquid
Teaching Materials | 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 Lennard-Jones potential.This assignment is to be...
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Computational Nanoscience, Lecture 1: Introduction to Computational Nanoscience
Teaching Materials | 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"...
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Computational Nanoscience, Lecture 6: Pair Distribution Function and More on Potentials
Teaching Materials | 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...
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Computational Nanoscience, Lecture 5: A Day of In-Class Simulation: MD of Carbon Nanostructures
Teaching Materials | 13 Feb 2008 | Contributor(s):: Jeffrey C Grossman, Elif Ertekin
In this lecture we carry out simulations in-class, 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...
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Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing
Teaching Materials | 13 Feb 2008 | 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 non-derivative methods are discussed, as well as the importance of the starting guess and how to find or generate good initial structures. We also briefly...
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Computational Nanoscience, Lecture 3: Computing Physical Properties
Teaching Materials | 11 Feb 2008 | 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...
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Overview of Computational Nanoscience: a UC Berkeley Course
Courses | 01 Feb 2008 | Contributor(s):: Jeffrey C Grossman, Elif Ertekin
This course will provide students with the fundamentals of computational problem-solving 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...