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Application-driven Co-Design: Using Proxy Apps in the ASCR Materials Co-Design Center
31 May 2012 | Online Presentations | Contributor(s): Jim Belak
Computational materials science is performed with a suite of applications that span the quantum mechanics of interatomic bonding to the continuum mechanics of engineering problems and phenomenon...
Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a Lennard-Jones Liquid
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14 Feb 2008 | Teaching Materials | 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...
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...
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. ...
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...
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...
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...
Computational Nanoscience, Lecture 17: Tight-Binding, and Moving Towards Density Functional Theory
21 Mar 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman
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...
Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
15 May 2008 | Teaching Materials | 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...
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...
Computational Nanoscience, Lecture 23: Modeling Morphological Evolution
In this lecture, we present an introduction to modeling the morphological evolution of materials systems. We introduce concepts of coarsening, particle-size distributions, the...
Computational Nanoscience, Lecture 26: Life Beyond DFT -- Computational Methods for Electron Correlations, Excitations, and Tunneling Transport
16 May 2008 | Teaching Materials | 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...
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...
Computational Nanoscience, Lecture 28: Wish-List, Reactions, and X-Rays.
16 May 2008 | Teaching Materials | 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...
Computational Nanoscience, Lecture 29: Verification, Validation, and Some Examples
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...
Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing
13 Feb 2008 | Teaching Materials | 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...
Computational Nanoscience, Lecture 5: A Day of In-Class Simulation: MD of Carbon Nanostructures
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...
Computational Nanoscience, Lecture 6: Pair Distribution Function and More on Potentials
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...
Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II
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...