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Overview of Computational Nanoscience: a UC Berkeley Course

Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II

This resource has a 9.7 Ranking

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Last 12 Months: updated 01 Oct, 2008
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Avg. Review: 5.0 out of 5 stars
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Contributor(s) Jeffrey C Grossman, Elif Ertekin
University of California, Berkeley
Abstract

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 approximation is described as well. We conclude with a few examples demonstrating the application of VMC and DMC to methane and ethane.

Credits Lucas K. Wagner
University of California, Berkeley
Cite this work

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  • Grossman, Jeffrey C; Ertekin, Elif (2008), "Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II," http://www.nanohub.org/resources/4566/.

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Date posted 20 May, 2008
Type Teaching Materials
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  1. 5.0 out of 5 stars 

    Posted on 25 June, 2008 by Anonymous

See also

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  • 9.6 Ranking Courses Part of: Overview of Computational Nanoscience: a UC Berkeley Course

    Overview of Computational Nanoscience: a UC Berkeley Course

    Type Courses
    Contributor(s) Jeffrey C Grossman, Elif Ertekin
    Date 01 Feb, 2008
    Avg. Rating 5.0 out of 5 stars  (4)
    Rate this

    This course will provide students with the fundamentals of computational problem-solving techniques that are used to understand and predict properties of nanoscale systems. Emphasis will be placed on how to use simulations effectively, intelligently, and cohesively to predict properties that occur …

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