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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.

All Categories (1-20 of 116)

  1. How to do 3D quantum monte carlo in Silvaco Atlas

    Closed | Responses: 0

    I am trying to simulation LER, RDF, variation of Fin height and fin thickness of tri-gate FINFET. I have access to Silvaco atlas. Do anybody has experience on 3D monter carlo in Silvac Atlas. I...

    http://nanohub.org/answers/question/822

  2. Archimedes, GNU Monte Carlo simulator

    29 May 2008 | Tools | Contributor(s): Jean Michel D Sellier

    GNU Monte Carlo simulation of 2D semiconductor devices, III-V materials

    http://nanohub.org/resources/archimedes

  3. 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

  4. 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

  5. BioMOCA Demo

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

    Transport Monte Carlo simulation of conduction in biological Ionic Channels.

    http://nanohub.org/resources/biomoca

  6. BioMOCA Suite

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

    Simulates ion flow through a channel.

    http://nanohub.org/resources/BMCsuite

  7. Buddy Damm

    I am interested in applying computational materials science tools to practicle issues in ferrous metallurgy in order to advance our understanding and applications of steels. My educational...

    http://nanohub.org/members/56465

  8. Bulk GaAs Ensemble Monte Carlo

    04 Apr 2006 | Tools | Contributor(s): Mohamed Mohamed, Anjali Bharthuar, Umberto Ravaioli

    Basic Ensemble Monte Carlo code for the study of transport in bulk GaAs semiconductor.

    http://nanohub.org/resources/moca-ensemble

  9. 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

  10. 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

  11. Carbon Nanotube Electronics: Modeling, Physics, and Applications

    28 Jun 2013 | Publications | Contributor(s): Jing Guo

    In recent years, significant progress in understanding the physics of carbon nanotube electronic devices and in identifying potential applications has occurred. In a nanotube, low bias transport...

    http://nanohub.org/resources/18742

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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...

    http://nanohub.org/resources/4035

  18. 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

  19. 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

  20. 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

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