Berkeley Computational Nanoscience Class Tools

Tools for UC Berkeley Computational Nanoscience course, Spring 2008

Launch Tool

This tool version is unpublished and cannot be run. If you would like to have this version staged, you can put a request through HUB Support.

Archive Version 2.0
Published on 03 Mar 2009, unpublished on 06 Mar 2009 All versions

doi:10.4231/D3DR2P802 cite this



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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.


Development Team: David Strubbe, Daniel Richards, Elif Ertekin, Jeff Grossman.

Cite this work

Researchers should cite this work as follows:

  • daniel richards; Elif Ertekin; Jeffrey C Grossman; David Strubbe (2017), "Berkeley Computational Nanoscience Class Tools," (DOI: 10.4231/D3DR2P802).

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