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. How to do 3D quantum monte carlo in Silvaco Atlas

    Q&A|Closed | Responses: 0

    I am trying to simulation LER, RDF, variation of Fin height and fin thickness of tri-gate FINFET. I have access to Silvaco atlas. Do anybody has experience on 3D monter carlo in Silvac Atlas. I...

    https://nanohub.org/answers/question/822

  2. Anisotropic Schrödinger Equation Quantum Corrections for 3D Monte Carlo Simulations of Nanoscale Multigate Transistors

    05 Jan 2016 | | Contributor(s):: Karol Kalna, Muhammad Ali A. Elmessary, Daniel Nagy, Manuel Aldegunde

    IWCE 2015 presentation. We incorporated anisotropic 2D Schrodinger equation based quantum corrections (SEQC) that depends on valley orientation into a 3D Finite Element (FE) Monte Carlo (MC) simulation toolbox. The MC toolbox was tested against experimental ID-VG characteristics of the 22 nm gate...

  3. Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?

    14 Aug 2017 | | Contributor(s):: Hanjing Xu, Pradeep Kumar Gurunathan

    Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions...

  4. Archimedes, GNU Monte Carlo simulator

    29 May 2008 | | Contributor(s):: Jean Michel D Sellier

    GNU Monte Carlo simulation of 2D semiconductor devices, III-V materials

  5. Atomistic Modeling: Past, Present, and Future, MGI, ICME, etc.

    03 Nov 2015 | | Contributor(s):: Paul Saxe

    I will present a perspective on atomistic modeling — tools using quantum methods such as DFT, as well as molecular dynamics and Monte Carlo methods based on forcefields — over the past 30 years or so. While we are all caught up in the present, it is important to remember and realize...

  6. Atomistic Simulations of Reliability

    01 Jul 2010 | | 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 been researched as a major cause of reliability degradation observed in intra-die and die-to-die...

  7. Austin Iglesias Saragih

    Austin Saragih is an undergraduate student studying Industrial Engineering at Purdue University. He is a 2013 NCN Summer Undergraduate Research Fellow.

    https://nanohub.org/members/82369

  8. Band Structure Lab: First-Time User Guide

    15 Jun 2009 | | 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 other aspects. Additionally, we provide explanations of the input settings and the results of the...

  9. Bhupesh Bishnoi

    https://www.iitk.ac.in/new/bhupesh-bishnoi

    https://nanohub.org/members/62512

  10. biomoca

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

    Ion channel simulator

  11. BioMOCA Suite

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

    Simulates ion flow through a channel.

  12. Buddy Damm

    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...

    https://nanohub.org/members/56465

  13. Bulk Heterojunction Morphology Generator

    11 Feb 2013 | | Contributor(s):: Michael C. Heiber

    This tool creates nanoscale bulk heterojunction morphologies for use with organic photovoltaics simulations

  14. 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...

  15. Bulk Monte Carlo Lab

    27 Apr 2008 | | Contributor(s):: Dragica Vasileska, Mark Lundstrom, Stephen M. Goodnick, Gerhard Klimeck

    This tool calculates the bulk values of the carrier drift velocity and average electron energy in any material in which the conduction band is represented by a three valley model. Examples include Si, Ge and GaAs.

  16. Bulk Monte Carlo Learning Materials

    By completing the Bulk Monte Carlo Lab exercises and tests, users will be able to: a) understand the way the Boltzmann Transport Equation (BTE) is solved using the Monte Carlo method, b) the...

    https://nanohub.org/wiki/BMC

  17. Bulk Monte Carlo: Implementation Details and Source Codes Download

    01 Jun 2010 | | 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 numerous books and reviews. In application to transport problems, a random walk is generated to...

  18. Carbon Nanotube Electronics: Modeling, Physics, and Applications

    30 Oct 2006 | | Contributor(s):: Jing Guo

    In recent years, significant progress in understanding the physics of carbon nanotube electronic devices and in identifying potential applications has occurred. In a nanotube, low bias transport can be nearly ballistic across distances of several hundred nanometers. Deposition of high-κ...

  19. Carbon Nanotube Electronics: Modeling, Physics, and Applications

    27 Jun 2013 | | Contributor(s):: Jing Guo

    In recent years, significant progress in understanding the physics of carbon nanotube electronic devices and in identifying potential applications has occurred. In a nanotube, low bias transport can be nearly ballistic across distances of several hundred nanometers. Deposition of high-k gate...

  20. Chih-Wei Lai

    https://nanohub.org/members/58478