# 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…

## 3 Citations

Artificial intelligence for search and discovery of quantum materials

- Communications Materials
- 2021

Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum…

Atomistic Line Graph Neural Network for improved materials property predictions

- Physicsnpj Computational Materials
- 2021

Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.…

Recent Advances and Applications of Deep Learning Methods in Materials Science

- Physics
- 2021

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows…

## References

SHOWING 1-10 OF 74 REFERENCES

Computational search for magnetic and non-magnetic 2D topological materials using unified spin–orbit spillage screening

- Physics, Materials Sciencenpj Computational Materials
- 2020

Two-dimensional topological materials (2D TMs) have a variety of properties that make them attractive for applications including spintronics and quantum computation. However, there are only a few…

High-throughput search for magnetic and topological order in transition metal oxides

- Physics, Materials ScienceScience 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

- Physics, MedicineScientific 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.

Intrinsic magnetic topological insulators in van der Waals layered MnBi2Te4-family materials

- Physics, BiologyScience 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

- Materials Science, 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, MedicineNature
- 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.

- Medicine, PhysicsPhysical 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

- Materials Science
- 2012

Empirical databases of crystal structures and thermodynamic properties are fundamental tools for materials research. Recent rapid proliferation of computational data on materials properties presents…

Topological quantum chemistry

- Medicine, 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.

Giant anomalous Hall effect in a ferromagnetic Kagomé-lattice semimetal

- Physics, Materials ScienceNature physics
- 2018

Electrical transport measurements reveal that Co3Sn2S2 is probably a magnetic Weyl semimetal, and hosts the highest simultaneous anomalous Hall conductivity and anomalies angle, driven by the strong Berry curvature near the Weyl points.