Tags: Monte Carlo

Description

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. Bulk Monte Carlo Code Described

    02 Jul 2008 | Teaching Materials | Contributor(s): Dragica Vasileska

    In this tutorial we give implementation details for the bulk Monte Carlo code for calculating the electron drift velocity, velocity-field characteristics and average carrier energy in bulk GaAs...

    https://nanohub.org/resources/4843

  2. Consistent Parameter Set for an Ensemble Monte Carlo Simulation of 4H-SiC

    02 Jul 2008 | Papers | Contributor(s): Dragica Vasileska

    A consistent parameter set is presented for Ensemble Monte Carlo simulation that simultaneously reproduces the experimental low-field and high-field characteristic transport parameters of 4H...

    https://nanohub.org/resources/4845

  3. Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I

    20 May 2008 | Teaching Materials | 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...

    https://nanohub.org/resources/4564

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

    20 May 2008 | Teaching Materials | 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...

    https://nanohub.org/resources/4566

  5. Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology

    20 May 2008 | Teaching Materials | Contributor(s): Elif Ertekin, Jeffrey C Grossman

    In this lecture, we describe the challenges in simulating water and introduce both explicit and implicit approaches. We also briefly describe protein structure, the Levinthal paradox, and...

    https://nanohub.org/resources/4576

  6. 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

    https://nanohub.org/resources/4473

  7. 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...

    https://nanohub.org/resources/4403

  8. biomoca

    14 Mar 2008 | Tools | Contributor(s): Reza Toghraee, Umberto Ravaioli

    Ion channel simulator

    https://nanohub.org/resources/biomoca

  9. 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. ...

    https://nanohub.org/resources/4134

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

    05 Mar 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...

    https://nanohub.org/resources/4090

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

    20 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...

    https://nanohub.org/resources/4067

  12. 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...

    https://nanohub.org/resources/4044

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

    15 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...

    https://nanohub.org/resources/4056

  14. BioMOCA Suite

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

    Simulates ion flow through a channel.

    https://nanohub.org/resources/BMCsuite

  15. 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...

    https://nanohub.org/resources/4035

  16. MIT Atomic Scale Modeling Toolkit

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

    Tools for Atomic Scale Modeling

    https://nanohub.org/resources/ucb_compnano

  17. 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

    https://nanohub.org/resources/qwalk

  18. 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

    https://nanohub.org/resources/moca

  19. QuaMC2D

    23 Feb 2007 | Tools | Contributor(s): Shaikh S. Ahmed, Dragica Vasileska

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

    https://nanohub.org/resources/quamc2d

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

    16 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...

    https://nanohub.org/resources/1973