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DFT with SIESTA, Data Visualization, and a Sophomore-level CURE with the MIT Atomic-Scale Modeling Toolkit
Online Presentations | 09 Apr 2024 | Contributor(s):: David A Strubbe
This presentation will focus on use of the density-functional theory (DFT) code SIESTA and visualization code XCrySDen, for calculations of structure, density, and wavefunctions, and visualization of these quantities as well as of Brillouin zones and Fermi surfaces. He uses the toolkit for a...
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Chemistry and Materials with the Amsterdam Modeling Suite
Online Presentations | 19 Apr 2023 | Contributor(s):: Nicolas Onofrio
In this talk, I will give an overview of the Amsterdam Modeling Suite to perform atomistic simulations at various levels of theory.
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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.
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Interactive Modeling of Materials with Density Functional Theory Using the Quantum ESPRESSO Interface within the MIT Atomic Scale Modeling Toolkit
Online Presentations | 22 Nov 2022 | Contributor(s):: Enrique Guerrero
We will explore the Quantum ESPRESSO interface within the MIT Atomic-Scale Modeling Toolkit with interactive examples. We will review the basics of density functional theory and then focus on the tool’s capabilities.
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Density Functional Theory: Introduction and Applications
Online Presentations | 07 Nov 2022 | Contributor(s):: André Schleife
In this webinar, Dr. Schleife will briefly outline the fundamentals of DFT, and demonstrate how to use Quantum Espresso in nanoHUB to compute electronic structure, electronic densities of state, total energies, and bulk modulus for example materials.
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CHEM 870 Tutorial 6b: Binding Energy, DFT, and CO2 Capture II
Online Presentations | 04 Sep 2022 | Contributor(s):: Nicole Adelstein
The main goal of these activities is to calculate the binding energy of CO2 to linker molecules in metal organic frameworks (MOFs). CO2 is a greenhouse gas. One necessary component of combating climate change is removing CO2 from the atmosphere. We will use density functional theory (DFT)...
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URE Experience - DFT Thermoelectric Calculations
Online Presentations | 15 Apr 2022 | Contributor(s):: Gustavo Javier
Gustavo discusses his experience in the 2015 NCN URE program and his work to develop a thermoelectric simulation for the nanoHBU tool DFT Material Properties Simulator . Gustavo Javier now teaches high school physics in the Los Angeles area.The DFT Material Properties Simulator can compute...
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CHEM 870 Tutorial 6a: Binding Energy, DFT, and CO2 Capture I
Online Presentations | 20 Dec 2021 | Contributor(s):: Nicole Adelstein
The main goal of these activities is to calculate the binding energy of CO2 to linker molecules in metal organic frameworks (MOFs). CO2 is a greenhouse gas. One necessary component of combating climate change is removing CO2 from the atmosphere. We will use density functional theory (DFT)...
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A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery
Online Presentations | 09 Sep 2021 | Contributor(s):: Anjana Talapatra
In this tutorial, we illustrate this approach using the example of wide band gap oxide perovskites. We will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator...
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Debugging Neural Networks
Online Presentations | 09 Sep 2021 | Contributor(s):: Rishi P Gurnani
The presentation will start with an overview of deep learning theory to motivate the logic in NetDebugger and end with a hands-on NetDebugger tutorial involving PyTorch, RDKit, and polymer data
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IWCN 2021: Quantum Transport Simulation on 2D Ferroelectric Tunnel Junctions
Online Presentations | 15 Jul 2021 | Contributor(s):: Eunyeong Yang, Jiwon Chang
In this work, we consider a simple asymmetric structure of metal-ferroelectric-metal (MFM) FTJs with two different ferroelectric materials, Hf0.5Zr0.5O2(HZO) and CuInP2S6(CIPS), respectively. To investigate the performance of FTJs theoretically, we first explore complex band structures of HZO...
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IWCN 2021: Density Functional Theory Modeling of Chemical Reactions at Interfaces
Online Presentations | 15 Jul 2021 | Contributor(s):: Namita Narendra, Jessica Wang, James Charles, Tillmann Christoph Kubis
In this work, we introduce a DFT-based method to predict energies of solute molecules in bulk solution and in various distances to solvent/air interfaces. The solute and all solvent molecules (~1400 atoms) are explicitly considered, and their electrons solved self-consistently in density...
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IWCN 2021: Ab initio Quantum Transport Simulation of Lateral Heterostructures Based on 2D Materials: Assessment of the Coupling Hamiltonians
Online Presentations | 14 Jul 2021 | Contributor(s):: Adel Mfoukh, Marco Pala
Lateral heterostructures based on lattice-matched 2D materials are a promising option to design efficient electron devices such as MOSFETs [1], tunnel-FETs [2] and energy-filtering FETs [3]. In order to rigorously describe the transport through such heterostructures, an ab-initio approach based...
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IWCN 2021: Thermoelectric Properties of Complex Band and Nanostructured Materials
Online Presentations | 14 Jul 2021 | Contributor(s):: Neophytos Neophytou, Patrizio Graziosi, Vassilios Vargiamidis
In this work, we describe a computational framework to compute the electronic and thermoelectric transport in materials with multi-band electronic structures of an arbitrary shape by coupling density function theory (DFT) bandstructures to the Boltzmann Transport Equation (BTE).
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Active Learning via Bayesian Optimization for Materials Discovery
Online Presentations | 25 Jun 2021 | Contributor(s):: Hieu Doan, Garvit Agarwal
In this tutorial, we will demonstrate the use of active learning via Bayesian optimization (BO) to identify ideal molecular candidates for an energy storage application.
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FDNS21: Revealing the Full Spectrum of 2D Materials with Superhuman Predictive Abilities
Online Presentations | 20 May 2021 | Contributor(s):: Evan Reed
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FDNS21: Predictive Models in Materials Making, 2D, 3D, 2.1D
Online Presentations | 27 Apr 2021 | Contributor(s):: Boris I Yakobson
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Convenient and efficient development of Machine Learning Interatomic Potentials
Online Presentations | 09 Mar 2021 | Contributor(s):: Yunxing Zuo
This tutorial introduces the concepts of machine learning interatomic potentials (ML-IAPs) in materials science, including two components of local environment atomic descriptors and machine learning models.
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Machine Learning Framework for Impurity Level Prediction in Semiconductors
Online Presentations | 15 Dec 2020 | Contributor(s):: Arun Kumar Mannodi Kanakkithodi
In this work, we perform screening of functional atomic impurities in Cd-chalcogenide semiconductors using high-throughput computations and machine learning.
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Simulating Electronic Properties of Materials Using Ab Initio Modeling with SIESTA on nanoHUB.org
Online Presentations | 08 Oct 2020 | Contributor(s):: Lan Li
The simulation tool featured in this presentation is MIT Atomic-Scale Modeling Toolkit.