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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"...
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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 Slater-Jastrow expansion of the wavefunction, and...
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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...
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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, particle-size distributions, the Lifshitz-Slyozov-Wagner model, thin film growth modes (Layer-by-Layer, Island growth, and Stranski-Krastanov), and...
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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 self-energy corrects the quasiparticle excitations energies predicted by Kohn-Sham DFT. For...
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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...
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Computational Nanoscience, Lecture 28: Wish-List, Reactions, and X-Rays.
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 chain-of-state methods such as the Nudged Elastic Band for determining energy barriers. The use of empirical, QM/MM methods are described. We...
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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
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Computational Nanoscience, Lecture 2: Introduction to Molecular Dynamics
30 Jan 2008 | | 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...
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Computational Nanoscience, Lecture 3: Computing Physical Properties
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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Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing
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 5: A Day of In-Class Simulation: MD of Carbon Nanostructures
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 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...
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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...
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Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation
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, Pop-Quiz
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
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Computational Nanoscience, Pop-Quiz Solutions
15 May 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman
The solutions to the pop-quiz are given in this handout.University of California, Berkeley
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Computer in Science Engineering: featuring nanoHUB.org
20 Apr 2010 |
The current issue of Computing in Science and Engineering focuses on cyber-enabled nanotechnology, and nanoHUB.org is featured extensively throughout.
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Cyber Infrastructure Days at Purdue University
31 Jan 2011 |
Purdue CI Days 2010 showcases technologies to enhance research, teaching and research funding. The program focus is on how just about any faculty member, research staffer, or graduate student can benefit from these technologies.
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Density Functional Theory: Introduction and Applications
01 Nov 2022 | | Contributor(s):: André Schleife
In this webinar, Dr. Schleife will briefly outline the fundamentals of DFT, and demonstrate how to use Quantum Espresso in nanoHUB to compute electronic structure, electronic densities of state, total energies, and bulk modulus for example materials.