Tags: Monte Carlo

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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.

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  1. Accelerating Radiation Damage Simulation Through Machine Learning

    Online Presentations | 21 May 2024 | Contributor(s):: Vinay Gupta, Shrienidhi Gopalakrishnan, Brian Hyun-jong Lee, Alejandro Strachan

    This study explores the challenge of material degradation from radiation exposure, a phenomenon that significantly impacts fields ranging from materials science to nuclear engineering and space exploration. As of today, the primary solution of conventional simulation techniques are...

  2. SCALE Electronics, Photonics, and Space, Oh My! - An Introduction to the EPICA Program

    Online Presentations | 02 Jan 2024 | Contributor(s):: Hannah Dattilo

  3. Teaching and Learning with the MIT Atomic Scale Modeling Toolkit's Classical and Quantum Atomic Modeling Applications

    Online Presentations | 23 Dec 2022 | Contributor(s):: Enrique Guerrero

     We will perform molecular dynamics computations using LAMMPS, simple Monte Carlo simulations including the Ising model, and run quantum chemistry and density functional theory computations.

  4. Nongnuch Artrith

    Dr. rer. nat. Nongnuch Artrith (http://nartrith.atomistic.net) is a Tenure-Track Assistant Professor in the Materials Chemistry and Catalysis group at the Debye Institute for Nanomaterials Science,...

    https://nanohub.org/members/384244

  5. Solving the 2D Ising Model

    Tools | 01 Nov 2022 | Contributor(s):: Ava Hallberg, George Maxwell Nishibuchi, Kat Nykiel, Alejandro Strachan

    Using Markov Chain Monte Carlo Method to visualize magnetism via Ising Model

  6. Machine Learning Predicts Additive Manufacturing Part Quality: Tutorial on Support Vector Regression

    Online Presentations | 26 Aug 2022 | Contributor(s):: Davis McGregor

    This tutorial introduces and demonstrates the use of machine learning (ML) to address this need. Using data collected from an AM factory, you will train a support vector regression (SVR) model to predict the dimensions of AM parts based on the design geometry and manufacturing parameters.

  7. Monte Carlo Electron Dynamics

    0.0 out of 5 stars 

    Tools | 21 Aug 2008 | Contributor(s):: Shaikh S. Ahmed, Zichang Zhang, Khadija Abul Khair, Sharnali Islam, Mohammad Zunaidur Rashid

    Simulates non-stationary electron transport in emerging semiconductors using Monte Carlo approach. Models how particle distribution function evolves in time and allows the user to extract velocity-field and mobility characteristics.

  8. Monte Carlo HEMT Simulator

    0.0 out of 5 stars 

    Tools | 30 Aug 2019 | Contributor(s):: Shaikh S. Ahmed, Mohammad Zunaidur Rashid, Khadija Abul Khair

    Simulates the current-voltage (I-V) and related characteristics of a nitride-based HEMT device using the 3-D particle-based Monte Carlo approach.

  9. IWCN 2021: Effective Monte Carlo Simulator of Hole Transport in SiGe alloys

    0.0 out of 5 stars 

    Online Presentations | 21 Jul 2021 | Contributor(s):: Caroline dos Santos Soares, Alan Rossetto, Dragica Vasileska, Gilson Wirth

    In this work, an Ensemble Monte Carlo (EMC) transport simulator is presented for simulation of hole transport in SiGe alloys.

  10. IWCN 2021: Computational Research of CMOS Channel Material Benchmarking for Future Technology Nodes: Missions, Learnings, and Remaining Challenges

    0.0 out of 5 stars 

    Online Presentations | 13 Jul 2021 | Contributor(s):: raseong kim, Uygar Avci, Ian Alexander Young

    In this preentation, we review our journey of doing CMOS channel material benchmarking for future technology nodes. Through the comprehensive computational research for past several years, we have successfully projected the performance of various novel material CMOS based on rigorous physics...

  11. MIT Atomic-Scale Modeling Toolkit

    4.5 out of 5 stars 

    Tools | 15 Jan 2008 | Contributor(s):: David A Strubbe, Enrique Guerrero, daniel richards, Elif Ertekin, Jeffrey C Grossman, Justin Riley

    Tools for Atomic-Scale Modeling

  12. Ruslan Allayarov

    https://nanohub.org/members/278942

  13. Gibbs Adsorption Simulator

    0.0 out of 5 stars 

    Tools | 23 Sep 2019 | Contributor(s):: Julian C Umeh, Thomas A Manz

    Simulates the adsorption of gases using Gibbs ensemble

  14. Mixed Gas Diffusion Calculator

    0.0 out of 5 stars 

    Tools | 25 Jun 2019 | Contributor(s):: Julian C Umeh, Thomas A Manz

    Simulates the diffusion of a gas mixture onto a metal organic framework

  15. Mixed Gas Adsorption Calculator

    0.0 out of 5 stars 

    Tools | 21 Jun 2019 | Contributor(s):: Julian C Umeh, Thomas A Manz

    This tool calculated the average adsorption of a gas mixture

  16. VLE Simulator

    0.0 out of 5 stars 

    Tools | 10 Jun 2019 | Contributor(s):: Julian C Umeh, Thomas A Manz

    Simulates the vapor liquid equilibrium of the first five Alkanes

  17. Gas Adsorption Calculator

    0.0 out of 5 stars 

    Tools | 07 Mar 2019 | Contributor(s):: Julian C Umeh, Thomas A Manz

    Simulates gas adsorption onto metal organic frameworks

  18. Thomas A Manz

    Tom Manz is a Chemical & Materials Engineering faculty at New Mexico State University. His research group develops new computational chemistry methods and physical interaction models. He is the...

    https://nanohub.org/members/222347

  19. Composite Filament Simulation 3D

    5.0 out of 5 stars 

    Tools | 06 Aug 2018 | Contributor(s):: Zachary Yun, Michelle Zhang, Ganesh Vurimi, Hayden Taylor, Sixian Jia

    Simulate electrical properties of a nanowire composite filament.

  20. Peter Koval

    https://nanohub.org/members/215617