High-throughput search for magnetic topological materials using spin-orbit spillage, machine learning, and experiments
@article{Choudhary2021HighthroughputSF, title={High-throughput search for magnetic topological materials using spin-orbit spillage, machine learning, and experiments}, author={Kamal Choudhary and Kevin F Garrity and Nirmal J. Ghimire and Naween Anand and Francesca Tavazza}, journal={Physical Review B}, year={2021}, volume={103} }
Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation, but very few high-quality candidate materials are known. In this work, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from 40000 three-dimensional materials in the JARVIS-DFT database (https://jarvis.nist.gov/jarvisdft). First, we screen materials with net…
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References
SHOWING 1-10 OF 76 REFERENCES
High-throughput search for magnetic and topological order in transition metal oxides
- Materials Science, PhysicsScience Advances
- 2020
This work performs a high-throughput band topology analysis of centrosymmetric magnetic materials, calculates topological invariants, and identifies 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagneticTopological insulators.
High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage
- PhysicsScientific Reports
- 2019
A novel methodology to identify topologically non-trivial materials based on band inversion induced by spin-orbit coupling (SOC) effect is presented, which is applicable to the investigation of disordered or distorted as well as magnetic materials, because it is not based on symmetry considerations.
Comprehensive search for topological materials using symmetry indicators
- Physics, Materials ScienceNature
- 2019
An algorithm based on symmetry indicators is used to search a crystallographic database and finds thousands of candidate topological materials, which could be exploited in next-generation electronic devices.
A complete catalogue of high-quality topological materials
- Materials ScienceNature
- 2019
Using a recently developed formalism called topological quantum chemistry, a high-throughput search of ‘high-quality’ materials in the Inorganic Crystal Structure Database is performed and it is found that more than 27 per cent of all materials in nature are topological.
Intrinsic magnetic topological insulators in van der Waals layered MnBi2Te4-family materials
- PhysicsScience Advances
- 2019
This work predicts a series of van der Waals layered MnBi2Te4-related materials that show intralayer ferromagnetic and interlayer antiferromagnetic exchange interactions that could profoundly change future research and technology of topological quantum physics.
The study of magnetic topological semimetals by first principles calculations
- Physicsnpj Computational Materials
- 2019
Magnetic topological semimetals (TSMs) are topological quantum materials with broken time-reversal symmetry (TRS) and isolated nodal points or lines near the Fermi level. Their topological properties…
Prediction and observation of an antiferromagnetic topological insulator
- Physics, Materials ScienceNature
- 2019
An intrinsic antiferromagnetic topological insulator, MnBi2Te4, is theoretically predicted and then realized experimentally, with implications for the study of exotic quantum phenomena.
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape.
- Computer SciencePhysical review materials
- 2018
It is demonstrated that the combination of pairwise radial, nearest neighbor, bond-angle, dihedral-angle and core-charge distributions plays an important role in predicting formation energies, bandgaps, static refractive indices, magnetic properties, and modulus of elasticity for three-dimensional materials as well as exfoliation energies of two-dimensional layered materials.
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
- Computer Science
- 2012
Topological quantum chemistry
- PhysicsNature
- 2017
A complete electronic band theory is proposed, which builds on the conventional band theory of electrons, highlighting the link between the topology and local chemical bonding and can be used to predict many more topological insulators.