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

    20 Aug 2008 | | 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

  2. Bulk Monte Carlo Code Described

    01 Jul 2008 | | 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 materials. Identical concepts with minor details apply to the development of a bulk Monte Carlo code...

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

    01 Jul 2008 | | 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 SiC.D. Vasileska and S. M. Goodnick, Computational Electronics, Morgan and Claypool, 2006.Freescale...

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

    15 May 2008 | | 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 the mean field approximation. We describe briefly the Slater-Jastrow expansion of the wavefunction,...

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

    15 May 2008 | | 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 of a functional form for the wavefunction. The DMC approach is explained, and the fixed node...

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

    16 May 2008 | | 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 simulations of proteins and protein structure using First Principles approaches and Monte Carlo...

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

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

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

  8. Practical Introduction to the BioMOCA Suite

    23 Apr 2008 | | 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 the four subtools in the BioMOCA Suite.I do not cover in detail how the BioMOCA code works. If you...

  9. biomoca

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

    Ion channel simulator

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

    05 Mar 2008 | | 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. This assignment is to be completed following lecture 12 and using the "Hard Sphere Monte Carlo" and...

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

    25 Feb 2008 | | 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 discussions of Transition State Theory and basic KMC algorithms. A simulation of vacancy-mediated diffusion...

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

    19 Feb 2008 | | 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 of the simplest systems which exhibits an order-disorder phase transition, which we will explore with...

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

    15 Feb 2008 | | 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 relevant concepts in statistical mechanics is presented. Students will be introduced to the Metropolis...

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

    14 Feb 2008 | | 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 simulations of fullerene growth on spherical surfaces are presented. A discussion of meaningful...

  15. BioMOCA Suite

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

    Simulates ion flow through a channel.

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

    13 Feb 2008 | | 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 importance of the starting guess and how to find or generate good initial structures. We also briefly...

  17. MIT Atomic-Scale Modeling Toolkit

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

    Tools for Atomic-Scale Modeling

  18. QWalk Quantum Monte Carlo Tutorial

    15 Jun 2007 | | 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

  19. Illinois Tools: MOCA

    28 Mar 2007 | | Contributor(s):: Mohamed Mohamed, Umberto Ravaioli, Nahil Sobh, derrick kearney, Kyeong-hyun Park

    2D Full-band Monte Carlo (MOCA) Simulation for SOI-Based Device Structures

  20. QuaMC2D

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

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