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Berkeley Computational Nanoscience Class Tools

By Daniel Richards1, Elif Ertekin2, Jeffrey C Grossman3, David Strubbe4

1. University of California, Berkeley 2. University of Illinois at Urbana-Champaign 3. Massachusetts Institute of Technology 4. Massachusetts Institute of Technology (MIT)

Tools for UC Berkeley Computational Nanoscience course, Spring 2008

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Archive Version 2.7
Published on 20 Apr 2009, unpublished on 27 Apr 2009
Latest version: 4.3. All versions

doi:10.4231/D38S4JN9J cite this

This tool is closed source.

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Abstract

This toolkit complements the Berkeley Computational Nanoscience class lecture series.

This set of simulation tools has been developed for use with a course at U.C. Berkeley, taught by Jeffrey Grossman with TA David Strubbe, which provides students with the fundamentals of computational problem-solving techniques that are used to understand and predict properties of nanoscale systems. Emphasis is placed on how to use simulations effectively, intelligently, and cohesively to predict properties that occur at the nanoscale for real systems. The course is designed to present a broad overview of computational nanoscience and is therefore suitable for both experimental and theoretical researchers.

These tools will continue to be updated throughout the Spring term of 2009. The following simulations are run by the tool:

  • Averages and Error Bars
  • Molecular Dynamics (Lennard-Jones)
  • Molecular Dynamics (Carbon Nanostructures)
  • Monte Carlo (Hard Sphere)
  • Monte Carlo (Ising Model)
  • Quantum Chemistry (GAMESS)
  • Density Functional Theory (Siesta)
  • Quantum Monte Carlo (QWalk)

Any questions, comments, difficulties should be directed to David or Jeff.

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