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

TL;DR

This study employs high-throughput density functional theory, machine learning, and experimental validation to identify and characterize magnetic topological materials from a large database, advancing materials discovery for spintronics and quantum computing.

Contribution

It introduces a systematic high-throughput screening method combining spin-orbit spillage analysis, machine learning, and experimental validation to discover magnetic topological materials.

Findings

Identified 25 insulating and 564 metallic candidate materials.

Developed machine learning models to predict key properties of materials.

Experimentally synthesized and characterized select candidate materials.

Abstract

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 magnetic moment > 0.5 {\mu}B and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The spillage acts as a signature of spin-orbit induced band-inversion. Then, we carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall conductivities, surface bandstructures, and Fermi-surfaces to determine interesting topological characteristics of the screened compounds. We also train machine learning models for predicting the spillage, bandgaps, and magnetic moments of new compounds, to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.