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Tags: Monte Carlo


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 (21-40 of 55)

  1. Lecture 7: Initialization and Equilibrium

    05 Jan 2010 | Presentation Materials | Contributor(s): Ashlie Martini

    Topics: Initial positions Initial velocities Evaluating equilibrium

  2. ECE 656 Lecture 31: Monte Carlo Simulation

    01 Dec 2009 | Online Presentations | Contributor(s): Mark Lundstrom

    Outline: Introduction Review of carrier scattering Simulating carrier trajectories Free flight Collision Update after collision Putting it all together Summary

  3. ECE 656 Lecture 30: Balance Equation Approach III

    01 Dec 2009 | Online Presentations | Contributor(s): Mark Lundstrom

    Outline Carrier Temperature and Heat Flux Balance equations in 3D Heterostructures Summary

  4. 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...

  5. 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,...

  6. Illinois PHYS 466, Lecture 18: Kinetic Monte Carlo (KMC)

    04 May 2009 | Online Presentations | Contributor(s): David M. Ceperley, Omar N Sobh

  7. 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

  8. Quantum and Thermal Effects in Nanoscale Devices

    18 Sep 2008 | Online Presentations | Contributor(s): Dragica Vasileska

    To investigate lattice heating within a Monte Carlo device simulation framework, we simultaneously solve the Boltzmann transport equation for the electrons, the 2D Poisson equation to get the...

  9. 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.

  10. 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

  11. 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...

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

    01 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...

  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...

  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...

  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...

  16. 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

  17. 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...

  18. Bulk GaAs Ensemble Monte Carlo

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

    Basic Ensemble Monte Carlo code for study of electron transport in bulk GaAs

  19. biomoca

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

    Ion channel simulator

  20. 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. ..., 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.