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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.

Resources (1-20 of 54)

  1. Test for Monte Carlo Learning Module

    30 Jul 2011 | Teaching Materials | Contributor(s): Dragica Vasileska, Gerhard Klimeck

    this is a test for the MC Learning Module.

    http://nanohub.org/resources/11767

  2. Single Particle and Ensemble Monte Carlo Method

    30 Jun 2011 | Teaching Materials | Contributor(s): Dragica Vasileska

    This set of handwritten notes is part of the Semiconductor Transport class.

    http://nanohub.org/resources/11558

  3. Manual for the Generalized Bulk Monte Carlo Tool

    24 Jun 2011 | Teaching Materials | Contributor(s): Raghuraj Hathwar, Dragica Vasileska

    This manual describes the physics implemented behind the generalized bulk Monte Carlo tool.

    http://nanohub.org/resources/11474

  4. Generalized Monte Carlo Presentation

    20 Jun 2011 | Teaching Materials | Contributor(s): Dragica Vasileska

    This presentation goes along with the Bulk Monte Carlo tool on the nanoHUB that calculates transients and steady-state velocity-field characteristics of arbitrary materials such as Si, Ge, GaAs,...

    http://nanohub.org/resources/11425

  5. High Field Transport and the Monte Carlo Method for the Solution of the Boltzmann Transport Equation

    23 Jul 2010 | Teaching Materials | Contributor(s): Dragica Vasileska

    This set of slides first describes the path-integral solution of the BTE and then discusses in details the Monte Carlo Method for the Solution of the Boltzmann Transport Equation.

    http://nanohub.org/resources/9403

  6. Atomistic Simulations of Reliability

    06 Jul 2010 | Teaching Materials | Contributor(s): Dragica Vasileska

    Discrete impurity effects in terms of their statistical variations in number and position in the inversion and depletion region of a MOSFET, as the gate length is aggressively scaled, have...

    http://nanohub.org/resources/9253

  7. Bulk Monte Carlo: Implementation Details and Source Codes Download

    01 Jun 2010 | Teaching Materials | Contributor(s): Dragica Vasileska, Stephen M. Goodnick

    The Ensemble Monte Carlo technique has been used now for over 30 years as a numerical method to simulate nonequilibrium transport in semiconductor materials and devices, and has been the subject...

    http://nanohub.org/resources/9109

  8. From Semi-Classical to Quantum Transport Modeling: Particle-Based Device Simulations

    10 Aug 2009 | Teaching Materials | Contributor(s): Dragica Vasileska

    This set of powerpoint slides series provides insight on what are the tools available for modeling devices that behave either classically or quantum-mechanically. An in-depth description is...

    http://nanohub.org/resources/7214

  9. Band Structure Lab: First-Time User Guide

    15 Jun 2009 | Teaching Materials | Contributor(s): Abhijeet Paul, Benjamin P Haley, Gerhard Klimeck

    This document provides useful information about Band Structure Lab. First-time users will find basic ideas about the physics behind the tool such as band formation, the Hamiltonian description,...

    http://nanohub.org/resources/6935

  10. Homework Assignment for Bulk Monte Carlo Lab: Velocity vs. Field for Arbitrary Crystallographic Orientations

    21 Aug 2008 | Teaching Materials | Contributor(s): Dragica Vasileska, Gerhard Klimeck

    User needs to calculate and compare to experiment the velocity field characteristics for electrons in Si for different crystalographic directions and 77K and 300K temperatures.

    http://nanohub.org/resources/5321

  11. Homework Assignment for Bulk Monte Carlo Lab: Arbitrary Crystallographic Direction

    20 Aug 2008 | Teaching Materials | Contributor(s): Dragica Vasileska, Gerhard Klimeck

    This exercise teaches the users how the average carrier velocity, average carrier energy and vally occupation change with the application of the electric field in arbitrary crystalographic direction

    http://nanohub.org/resources/5275

  12. Bulk Monte Carlo Code Described

    01 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...

    http://nanohub.org/resources/4843

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

    15 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...

    http://nanohub.org/resources/4564

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

    15 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...

    http://nanohub.org/resources/4566

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

    16 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...

    http://nanohub.org/resources/4576

  16. 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. ...

    http://nanohub.org/resources/4134

  17. 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...

    http://nanohub.org/resources/4090

  18. 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...

    http://nanohub.org/resources/4067

  19. 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...

    http://nanohub.org/resources/4044

  20. 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...

    http://nanohub.org/resources/4056

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