# 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…

## 6 Citations

All topological bands of all nonmagnetic stoichiometric materials

- Materials Science, PhysicsScience
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Topological quantum chemistry and symmetry-based indicators have facilitated large-scale searches for materials with topological properties at the Fermi energy (EF). We report the implementation of a…

Machine Learning and Symbolic Regression for Adsorption of Atmospheric Molecules on Low-Dimensional TiO2

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Recent advances and applications of deep learning methods in materials science

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A high-level overview of deep learning methods followed by a detailed discussion of recent developments ofdeep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing is presented.

Artificial intelligence for search and discovery of quantum materials

- PhysicsCommunications Materials
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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…

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- Computer Sciencenpj Computational Materials
- 2021

An Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles, leading to improved performance on multiple atomistic prediction tasks.

Machine learning spectral indicators of topology

- Physics
- 2020

It is shown that XAS can potentially uncover materials' topology when augmented by machine learning, and the proposed machine learning-empowered XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials.

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