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

Nathan C Frey; Matthew K Horton; Jason M Munro; Sinéad M Griffin; Kristin A Persson; Vivek B Shenoy

doi:10.1126/sciadv.abd1076

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

Sci Adv. 2020 Dec 9;6(50):eabd1076. doi: 10.1126/sciadv.abd1076. Print 2020 Dec.

Authors

Nathan C Frey^{1

2}, Matthew K Horton^{2

3}, Jason M Munro², Sinéad M Griffin^{4

5}, Kristin A Persson^{2

3}, Vivek B Shenoy⁶

Affiliations

¹ Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
² Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
³ Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA 94704, USA.
⁴ Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
⁵ Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
⁶ Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. vshenoy@seas.upenn.edu.

Abstract

The discovery of intrinsic magnetic topological order in MnBi₂Te₄ has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder 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 more than 27,000 unique magnetic orderings for more than 3000 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.

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).