High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments

Published

April 16, 2021

Author(s)

Kamal Choudhary, Kevin Garrity, Francesca Tavazza, Nirmal Ghimire, Naween Anand

Abstract

Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation. We use a systematic high-throughput density functional theory to identify magnetic topological materials out of ≈ 40000 three-dimensional materials in the JARVIS-DFT database (https://jarvis.nist.gov/jarvisdft). First, we screen materials with net magnetic moment > 0.5 μB and spin-orbit spillage > 0.25 resulting in 26 insulating and 564 metallic candidates. The spillage act as a signature of band-inversion due to spin-orbit coupling effects. Then, we carry out Wannier charge centers, Chern number, anomalous Hall conductivity, surface bandstructure, and Fermi-surface calculations to determine interesting topological characteristics of the screened compounds. We also train machine learning model for predicting spillage, bandgap and magnetic moment to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.

Citation

Physical Review B

Pub Type

Journals

Download Paper

DOI Link

Keywords

JARVIS-DFT, Topological materials, quantum, DFT

Citation

Choudhary, K. , Garrity, K. , Tavazza, F. , Ghimire, N. and Anand, N. (2021), High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments, Physical Review B, [online], https://doi.org/10.1103/PhysRevB.103.155131 (Accessed October 12, 2024)