Find information on common issues.
Ask questions and find answers from other users.
Suggest a new site feature or improvement.
Check on status of your tickets.
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.
Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II
out of 5 stars
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...
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...
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
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...
30 May 2006 | | Contributor(s):: Reza Toghraee, Umberto Ravaioli
Ion channel simulator
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...
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...
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...
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...
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...
04 Feb 2008 | | 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 | | 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...
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
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
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
13 Mar 2006 | | Contributor(s):: Shaikh S. Ahmed, Dragica Vasileska
Quantum-corrected Monte-Carlo electron transport simulator for two-dimensional MOSFET devices.
Materials Science on the Atomic Scale with the 3-D Atom Probe
08 Nov 2006 |
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 manufacture. In recent years, these goals have come closer to realisation, thanks in part to the...
31 Oct 2006 | | Contributor(s):: , , Paul Dodd, M. A. Stettler, Xufeng Wang, Gerhard Klimeck
Improved program consists of DEMON and SDEMON
Homework for Monte Carlo Method: High field transport in Bulk Si
06 Jan 2006 | | Contributor(s):: Muhammad A. Alam
This homework assignment is part of ECE 656 "Electronic Transport in Semiconductors" (Purdue University). It contains 10 problems which lead students through the simulation of high-field transport in bulk silicon.
Review of Several Quantum Solvers and Applications
11 Jun 2004 | | Contributor(s):: Umberto Ravaioli
Review of Several Quantum Solvers and Applications