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Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Computational Materials Discovery and Optimization
Presentation Title Computational Screening of Novel Two-dimensional Topological Insulators and Layer-dependent Properties
Author(s) Kamal Choudhary, Kevin Garrity, Francesca Tavazza
On-Site Speaker (Planned) Kamal Choudhary
Abstract Scope Two dimensional topological insulators have immense potential for symmetry protected states, spintronics and quantum computer applications. However, only few of such materials are known at present hindering much development. In this work, we computationally screen new topological insulating materials among our database of ~1500 predicted 2D materials. These novel 2D materials were predicted based on relative error in lattice constants from experiment and DFT-PBE. We optimize the bulk structure with Vanderwaal vDW-optB88 functional, and then use SOC on the optimized structure. We use Wannier interpolation technique to calculate Z2 invariant index of the material. We the use similar method to investigate how number of layers (single, double -layer and bulk) influence electronic properties of such two-dimensional materials. The database is publicly available at our publicly available website www.ctcms.nist.gov/~knc6/JVASP.html
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Combined Experimental-computational Approach to Determining Nanoscale Structures
A Materials-informatics Approach for Finding New Hard-magnetic Phases
Computational Screening of Novel Two-dimensional Topological Insulators and Layer-dependent Properties
Data-driven Discovery of Photocathodes for CO2 Reduction
Design Concepts of Optimized MRI Magnet by COMSOL Multiphysics Simulation
Determination of Thermal Transport in Solids and Liquids by Non-equilibrium Molecular Dynamics Simulations
Dual Band Metamaterial Perfect Absorber Based on Mie Resonances
Economic Analysis of National Needs for Technology Infrastructure to Support the Materials Genome Initiative
Fabricating Optimized Crystallographic Textures through Heterogeneous Templated Grain Growth
First-principles Calculations on the Multiferroic Properties of Two-dimensional Oxides
First Principle Prediction of Magnetic Topological Phase in Thin Films of Bi2XY4 (X = Mn, Cr; Y = Se, Te)
High-throughput Investigation of the Electronic Properties of 2D and Bulk Materials in the MaterialsWeb Database
Holistic Computational Structure Screening of More than 12 000 Candidates for Solid Lithium-ion Conductor Materials
Improving the Ductility of Boron Carbide from Computational Design
L-27: Computational Design of Fatigue-resistant NiTi-based Shape Memory Alloys
Learning Grain Boundary Properties from Macroscopic and Microscopic Structural Descriptors
Light-metal Complex Hydrides: Computational Structure Prediction and Interaction with Functionalized Nanoporous Hosts
Machine Learning for Materials
Machine Learning for Prediction of Electronic Structures of Multi-component Alloys
Minimal Addition of Cerium for Stability of Critical Phases in Hard Magnetic AlNiCo Alloys: Combined Machine Learning and CALPHAD
Molecular Crystal Structure Prediction with Gator and Genarris
Predicting Ferroelectric Properties from Microstructures with Deep Learning
Quantum-accurate Force Fields from Machine Learning of Large Materials Data
Reentrant Melting of Sodium, Magnesium and Aluminum and Possible Universal Trend
Search for Rare-Earth Free Permanent Magnets in Fe and Co Based Compounds by Adaptive Genetic Algorithm
Software Tools for High-throughput Materials Data Generation and Data Mining
Structure-property Linkages for Porous Membranes Using the Materials Knowledge Systems Framework
Tailoring Properties in Multi-component Alloys through Heuristic Optimization
The Use of Cluster Expansions to Predict the Structure and Properties of Catalysts

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