Find information on common issues.
Ask questions and find answers from other users.
Suggest a new site feature or improvement.
Check on status of your tickets.
ABINIT: First-Time User Guide
09 Jun 2009 | Teaching Materials | Contributor(s): Benjamin P Haley
This first-time user guide provides an introduction to using ABINIT on nanoHUB. We include a very brief summary of Density Functional Theory along with a tour of the Rappture interface. We...
Computational Nanoscience, Homework Assignment 1: Averages and Statistical Uncertainty
5.0 out of 5 stars
30 Jan 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
The purpose of this assignment is to explore statistical errors and data correlation.
This assignment is to be completed following lectures 1 and 2 using the "Average" program in the Berkeley...
Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a Lennard-Jones Liquid
15 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
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
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
24 Mar 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 13: Introduction to Computational Quantum Mechanics
05 May 2008 | Teaching Materials | 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.
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 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 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...
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 19: Band Structure and Some In-Class Simulation: DFT for Solids
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...
Computational Nanoscience, Lecture 1: Introduction to Computational Nanoscience
15 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...
Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
20 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
20 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
20 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
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...