Tags: density functional theory (DFT)

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  1. A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery

    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...

  2. Debugging Neural Networks

    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

  3. Mark Schofield

    B.S. Chemistry & Biochemistry, University of Massachusetts at AmherstPh.D. Inorganic Chemistry, Massachusetts Institute of TechnologyPost-doc University of Chicago


  4. OctopusPY: Tool for Calculating Effective Mass from Octopus DFT Bandstructures

    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...

  5. Debugging Neural Networks

    07 Aug 2021 | | Contributor(s):: Rishi P Gurnani

    Debug common errors in neural networks.

  6. ML-aided High-throughput screening for Novel Oxide Perovskite Discovery

    15 Jul 2021 | | Contributor(s):: Anjana Talapatra

    ML-based tool to discover novel oxide perovskites with wide band gaps

  7. IWCN 2021: Quantum Transport Simulation on 2D Ferroelectric Tunnel Junctions

    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...

  8. IWCN 2021: Density Functional Theory Modeling of Chemical Reactions at Interfaces

    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...

  9. IWCN 2021: Ab initio Quantum Transport Simulation of Lateral Heterostructures Based on 2D Materials: Assessment of the Coupling Hamiltonians

    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...

  10. IWCN 2021: Thermoelectric Properties of Complex Band and Nanostructured Materials

    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).

  11. Active Learning via Bayesian Optimization for Materials Discovery

    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.

  12. The DFT calculation for Amorphous Silica is not able to process showing an error I am not able to understand.

    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 :...


  13. Muhammad Aminul Haque Chowdhury


  14. Anoop A Nair

    I'm an integrated masters student in physics at the Indian Institute of Science Education and Research -Thiruvananthapuram


  15. FDNS21: Revealing the Full Spectrum of 2D Materials with Superhuman Predictive Abilities

    20 May 2021 | | Contributor(s):: Evan Reed

  16. FDNS21: Predictive Models in Materials Making, 2D, 3D, 2.1D

    27 Apr 2021 | | Contributor(s):: Boris I Yakobson

  17. Convenient and efficient development of Machine Learning Interatomic Potentials

    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.

  18. MIT Atomic-Scale Modeling Toolkit

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

    Tools for Atomic-Scale Modeling

  19. DFT Results Explorer

    04 Oct 2020 | | Contributor(s):: Saaketh Desai, Juan Carlos Verduzco Gastelum, Daniel Mejia, Alejandro Strachan

    Use visualization tools to explore correlations in a DFT simulation results database

  20. Machine Learning Force Field for Materials

    27 Oct 2020 | | Contributor(s):: Chi Chen, Yunxing Zuo

    Machine learning force field for materials