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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.
Bulk Monte Carlo Code Described
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02 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...
Consistent Parameter Set for an Ensemble Monte Carlo Simulation of 4H-SiC
02 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...
Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
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20 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...
Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II
20 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...
Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology
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...
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
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...
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14 Mar 2008 | Tools | Contributor(s): Reza Toghraee, Umberto Ravaioli
Ion channel simulator
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
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 9: Hard-Sphere Monte Carlo In-Class Simulation
20 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...
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...
Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II
15 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...
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14 Feb 2008 | Tools | Contributor(s): David Papke, Reza Toghraee, Umberto Ravaioli, Ankit Raj
Simulates ion flow through a channel.
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...
MIT Atomic Scale Modeling Toolkit
24 Jan 2008 | Tools | Contributor(s): daniel richards, Elif Ertekin, Jeffrey C Grossman, David Strubbe, Justin Riley
Tools for Atomic Scale Modeling
QWalk Quantum Monte Carlo Tutorial
15 Jun 2007 | Tools | 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
Illinois Tools: MOCA
28 Mar 2007 | Tools | Contributor(s): Mohamed Mohamed, Umberto Ravaioli, Nahil Sobh, derrick kearney
A 2D Full-band Monte Carlo (MOCA) Simulation of SOI Device Structures
23 Feb 2007 | Tools | Contributor(s): Shaikh S. Ahmed, Dragica Vasileska
Quantum-corrected Monte-Carlo transport simulator for two-dimensional MOSFET devices.
Materials Science on the Atomic Scale with the 3-D Atom Probe
16 Nov 2006 | Online Presentations | Contributor(s): George D. W. Smith
Some of the key goals of materials science and technology are to be able to design a material from first principles, to predict its behaviour, and also to optimise the processing route for its...