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Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems. Because of their reliance on repeated computation of random or pseudo-random numbers, these methods are most suited to calculation by a computer and tend to be used when it is unfeasible or impossible to compute an exact result with a deterministic algorithm.

Learn more about quantum dots from the many resources on this site, listed below. More information on Monte Carlo method can be found here.

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  1. Molecular modeling of lipid bilayer edge and hybrid-MCMD method: Implementation and application

    29 Apr 2008 | Online Presentations | Contributor(s): Yong Jiang

    Introduction to mixed lipid systems, Hybrid Monte Carlo and MD (atomistic) algorithm for mixed lipid systems

  2. Practical Introduction to the BioMOCA Suite

    23 Apr 2008 | Online Presentations | Contributor(s): David Papke

    In this presentation, I describe how to use the online BioMOCA Suite. I explain how to prepare the .pqr input protein structure from a .pdb structure. I then explain in detail how to use each of...

  3. biomoca

    30 May 2006 | Tools | Contributor(s): Reza Toghraee, Umberto Ravaioli

    Ion channel simulator

  4. Computational Nanoscience, Homework Assignment 4: Hard-Sphere Monte Carlo and Ising Model

    05 Mar 2008 | Teaching Materials | 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. ...

  5. Computational Nanoscience, Lecture 10: Brief Review, Kinetic Monte Carlo, and Random Numbers

    25 Feb 2008 | Teaching Materials | 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...

  6. Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation

    19 Feb 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman

    In this lecture we carry out simulations in-class, with guidance from the instructors. We use the HSMC tool (within the nanoHUB simulation toolkit for this course). The hard sphere system is one...

  7. Computational Nanoscience, Lecture 7: Monte Carlo Simulation Part I

    15 Feb 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin

    The purpose of this lecture is to introduce Monte Carlo methods as a form of stochastic simulation. Some introductory examples of Monte Carlo methods are given, and a basic introduction to...

  8. Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II

    14 Feb 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman

    In this lecture, we continue our discussion of Monte Carlo simulation. Examples from Hard Sphere Monte Carlo simulations based on the Metropolis algorithm and from Grand Canonical Monte Carlo...

  9. BioMOCA Suite

    04 Feb 2008 | Tools | Contributor(s): David Papke, Reza Toghraee, Umberto Ravaioli, Ankit Raj

    Simulates ion flow through a channel.

  10. Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing

    13 Feb 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin

    In this lecture, we discuss various methods for finding the ground state structure of a given system by minimizing its energy. Derivative and non-derivative methods are discussed, as well as the...

  11. MIT Atomic Scale Modeling Toolkit

    15 Jan 2008 | Tools | Contributor(s): daniel richards, Elif Ertekin, Jeffrey C Grossman, David Strubbe, Justin Riley

    Tools for Atomic Scale Modeling

  12. QWalk Quantum Monte Carlo Tutorial

    15 Jun 2007 | Tools | Contributor(s): Lucas Wagner, Jeffrey C Grossman, Jeffrey B. Neaton, Ian Michael Rousseau

    An accurate method to calculate the many body ground state of electrons

  13. Illinois Tools: MOCA

    28 Mar 2007 | Tools | Contributor(s): Mohamed Mohamed, Umberto Ravaioli, Nahil Sobh, derrick kearney

    A 2D Full-band Monte Carlo (MOCA) Simulation of SOI Device Structures

  14. QuaMC2D

    13 Mar 2006 | Tools | Contributor(s): Shaikh S. Ahmed, Dragica Vasileska

    Quantum-corrected Monte-Carlo transport simulator for two-dimensional MOSFET devices.

  15. Materials Science on the Atomic Scale with the 3-D Atom Probe

    08 Nov 2006 | Online Presentations | Contributor(s): George D. W. Smith

    Some of the key goals of materials science and technology are to be able to design a material from first principles, to predict its behaviour, and also to optimise the processing route for its...

  16. demons

    31 Oct 2006 | Tools | Contributor(s): M. E. Klausmeier-Brown, C. M. Maziar, P. E. Dodd, M. A. Stettler, Xufeng Wang, Gerhard Klimeck

    Improved program consists of DEMON and SDEMON

  17. Homework for Monte Carlo Method: High field transport in Bulk Si

    06 Jan 2006 | Teaching Materials | Contributor(s): Muhammad A. Alam

    This homework assignment is part of ECE 656 "Electronic Transport in Semiconductors" (Purdue University). It contains 10 problems which lead students through the simulation of high-field...

  18. Review of Several Quantum Solvers and Applications

    11 Jun 2004 | Online Presentations | Contributor(s): Umberto Ravaioli

    Review of Several Quantum Solvers and Applications

  19. IWCE 2004 Held at Purdue

    24 Oct 2004 | Workshops

    IEEE and NCN sponsored the 10th International Workshop of Computational Electronics at Purdue, October 24-27, with the theme "The field of Computational Electronics - Looking back and looking ahead.", a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.