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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.
Archimedes, GNU Monte Carlo simulator
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29 May 2008 | Tools | Contributor(s): Jean Michel D Sellier
GNU Monte Carlo simulation of 2D semiconductor devices, III-V materials
Atomistic Simulations of Reliability
01 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 recently …
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, and …
30 May 2006 | Tools | Contributor(s): Reza Toghraee, Umberto Ravaioli
Transport Monte Carlo simulation of conduction in biological Ionic Channels.
04 Feb 2008 | Tools | Contributor(s): David Papke, Reza Toghraee, Umberto Ravaioli, Ankit Raj
Simulates ion flow through a channel.
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 …
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.
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 …
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 of …
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. …
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 …
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 the …
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 of …
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 …
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 …
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 relevant …
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 …
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 of …
Consistent Parameter Set for an Ensemble Monte Carlo Simulation of 4H-SiC
01 Jul 2008 | Publications | 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. …
31 Oct 2006 | Tools | Contributor(s): M. E. Klausmeier-Brown, C. M. Maziar, P. E. Dodd, M. A. Stettler, Xufeng Wang, Gerhard Klimeck
Improved program consists of DEMON and SDEMON
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