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Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Computational Materials Discovery and Optimization
Presentation Title First Principle Prediction of Magnetic Topological Phase in Thin Films of Bi2XY4 (X = Mn, Cr; Y = Se, Te)
Author(s) Sugata Chowdhury, Joseph A Hagmann, Curt A. Richter, Angela R. Hight Walker, Francesca Tavazza
On-Site Speaker (Planned) Sugata Chowdhury
Abstract Scope Theoretical and experimental studies regarding magnetic topological insulators (TIs) has experienced an enormous growth over last few years, but a relatively small number of material classes have experimentally shown the magnetic properties. Recently we have reported about the growth of the thin film of magnetic TI with stoichiometric composition Bi2MnSe4 by molecular beam epitaxy (MBE) and the electronic structure of Bi2MnSe4. In this work, we predicted three new magnetic TI materials (Bi2MnTe4, Bi2CrSe4, and Bi2CrTe4) using density functional theory (DFT). We calculated the formation energies, charge states, band structures, and magnetic properties of all these materials. Our calculations revealed that in the six-SL case, the valence band near the K point crosses the Fermi level, signifying an insulator-to-metal transition, and implying that Bi2XY4 with a thickness greater than six SL is a topologically non-trivial metal. Also, we calculated the concentration of magnetic atoms in TI materials for insulator-to-metal transition.
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
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Design Concepts of Optimized MRI Magnet by COMSOL Multiphysics Simulation
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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
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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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