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...
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
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...
07 Aug 2021 | | Contributor(s):: Rishi P Gurnani
Debug common errors in neural networks.
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
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...
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...
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 , tunnel-FETs  and energy-filtering FETs . In order to rigorously describe the transport through such heterostructures, an ab-initio approach based...
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).
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.
The DFT calculation for Amorphous Silica is not able to process showing an error I am not able to understand.
Closed | Responses: 1
Muhammad Aminul Haque Chowdhury
Anoop A Nair
FDNS21: Revealing the Full Spectrum of 2D Materials with Superhuman Predictive Abilities
20 May 2021 | | Contributor(s):: Evan Reed
FDNS21: Predictive Models in Materials Making, 2D, 3D, 2.1D
27 Apr 2021 | | Contributor(s):: Boris I Yakobson
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.
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
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
Machine Learning Force Field for Materials
27 Oct 2020 | | Contributor(s):: Chi Chen, Yunxing Zuo
Machine learning force field for materials