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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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Mark Schofield
B.S. Chemistry & Biochemistry, University of Massachusetts at AmherstPh.D. Inorganic Chemistry, Massachusetts Institute of TechnologyPost-doc University of Chicago
https://nanohub.org/members/337993
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OctopusPY: Tool for Calculating Effective Mass from Octopus DFT Bandstructures
Downloads | 16 Aug 2021 | Contributor(s):: Olivia M. Pavlic, Austin D. Fatt, Gregory T. Forcherio, Timothy A. Morgan, Jonathan Schuster
OctopusPY is a Python package supporting manipulation and analytic processing of electronic band structure data generated by the density functional theory (DFT) software Octopus. In particular, this package imports Octopus-calculated band structure for a given material and...
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Debugging Neural Networks
Tools | 07 Aug 2021 | Contributor(s):: Rishi P Gurnani
Debug common errors in neural networks.
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ML-aided High-throughput screening for Novel Oxide Perovskite Discovery
Tools | 15 Jul 2021 | Contributor(s):: Anjana Talapatra
ML-based tool to discover novel oxide perovskites with wide band gaps
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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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The DFT calculation for Amorphous Silica is not able to process showing an error I am not able to understand.
Q&A|Closed | Responses: 1
Whenever I am trying to perform DFT calculation of any molecule there comes the type of error which is not understandable. The recent one being for amorphous Silica stating " from pp_check_file :...
https://nanohub.org/answers/question/2483
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Muhammad Aminul Haque Chowdhury
https://nanohub.org/members/328958
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Anoop A Nair
I'm an integrated masters student in physics at the Indian Institute of Science Education and Research -Thiruvananthapuram
https://nanohub.org/members/328484
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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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Shigeki Yoshida
https://nanohub.org/members/318796
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MIT Atomic-Scale Modeling Toolkit
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