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ABINIT: First-Time User Guide
09 Jun 2009 | | 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 discuss the default simulation (what happens if you don't change any inputs, and just hit "simulate") as...
Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
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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,...
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...
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, Pop-Quiz Solutions
The solutions to the pop-quiz are given in this handout.University of California, Berkeley
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 Lifshitz-Slyozov-Wagner model, thin film growth modes (Layer-by-Layer, Island growth, and Stranski-Krastanov), and...
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...
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...
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...
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 concepts.University of California, Berkeley
Computational Nanoscience, Lecture 19: Band Structure and Some In-Class Simulation: DFT for Solids
30 Apr 2008 | | 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 12: In-Class Simulation of Ising Model
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...
Computational Nanoscience, Homework Assignment 4: Hard-Sphere Monte Carlo and Ising Model
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...
Computational Nanoscience, Lecture 10: Brief Review, Kinetic Monte Carlo, and Random Numbers
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...
Computational Nanoscience, Lecture 11: Phase Transitions and the Ising Model
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...