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

TL;DR

This study conducts a large-scale computational search for magnetic and topological phases in transition metal oxides, identifying new candidate materials and developing machine learning tools to predict complex quantum orders efficiently.

Contribution

It performs high-throughput calculations of magnetic and topological properties in thousands of oxides, discovering new topological materials and creating ML models for rapid prediction.

Findings

Identified 18 new magnetic topological materials.

Developed machine learning classifiers for magnetic and topological prediction.

Computed magnetic ground states and exchange parameters for over 3,000 oxides.

Abstract

The discovery of intrinsic magnetic topological order in $\rm MnBi_2Te_4$ has invigorated the search for materials with coexisting magnetic and topological phases. These multi-order quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute over 27,000 unique magnetic orderings for over 3,000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.