Tags: computational science/engineering

All Categories (1-20 of 79)

  1. Research projects for summer

    Closed | Responses: 0

    I am a sophomore in computer engineering and I wanted to know if there are any good research projects this summer in my field. If there are, could you please write the project and the professor...

    https://nanohub.org/answers/question/460

  2. 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...

  3. Are Simulation Tools Developed and Used by Experts Appropriate Experimentation Tools for Educational Contexts?

    08 Apr 2010 | | Contributor(s):: Alejandra J. Magana, Sean Brophy,

    Simulations and visualizations can lead to significant improvements in students'conceptual understanding. This increased understanding may be due to the formation of expert-like dynamic mental models. Laboratory simulations have been used in educational contexts forinquiry learning by allowing...

  4. Chanaka Suranjith Rupasinghe

    Chanaka Rupasinghe is a researcher of computational nanotechnology at Lanka Software Foundation and he works together with Sri Lanka Institute of Nanotechnology on open-source simulation and...

    https://nanohub.org/members/36739

  5. Computational Nanoscience, Homework Assignment 1: Averages and Statistical Uncertainty

    30 Jan 2008 | | 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 Toolkit.University of California, Berkeley

  6. Computational Nanoscience, Homework Assignment 2: Molecular Dynamics Simulation of a Lennard-Jones Liquid

    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...

  7. Computational Nanoscience, Homework Assignment 3: Molecular Dynamics Simulation of Carbon Nanotubes

    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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. Computational Nanoscience, Lecture 13: Introduction to Computational Quantum Mechanics

    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.

  13. Computational Nanoscience, Lecture 14: Hartree-Fock Calculations

    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.

  14. Computational Nanoscience, Lecture 15: In-Class Simulations: Hartree-Fock

    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.

  15. Computational Nanoscience, Lecture 16: More and Less than Hartree-Fock

    30 Apr 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    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...

  16. Computational Nanoscience, Lecture 18.5: A Little More, and Lots of Repetition, on Solids

    30 Apr 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    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.

  17. 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...

  18. 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"...

  19. 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,...

  20. 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...