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

The discovery of intrinsic magnetic topological order in MnBi(2)Te(4) 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...

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Autores principales: Frey, Nathan C., Horton, Matthew K., Munro, Jason M., Griffin, Sinéad M., Persson, Kristin A., Shenoy, Vivek B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725452/
https://www.ncbi.nlm.nih.gov/pubmed/33298446
http://dx.doi.org/10.1126/sciadv.abd1076
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author Frey, Nathan C.
Horton, Matthew K.
Munro, Jason M.
Griffin, Sinéad M.
Persson, Kristin A.
Shenoy, Vivek B.
author_facet Frey, Nathan C.
Horton, Matthew K.
Munro, Jason M.
Griffin, Sinéad M.
Persson, Kristin A.
Shenoy, Vivek B.
author_sort Frey, Nathan C.
collection PubMed
description The discovery of intrinsic magnetic topological order in MnBi(2)Te(4) 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.
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spelling pubmed-77254522020-12-16 High-throughput search for magnetic and topological order in transition metal oxides Frey, Nathan C. Horton, Matthew K. Munro, Jason M. Griffin, Sinéad M. Persson, Kristin A. Shenoy, Vivek B. Sci Adv Research Articles The discovery of intrinsic magnetic topological order in MnBi(2)Te(4) 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. American Association for the Advancement of Science 2020-12-09 /pmc/articles/PMC7725452/ /pubmed/33298446 http://dx.doi.org/10.1126/sciadv.abd1076 Text en 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). https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Frey, Nathan C.
Horton, Matthew K.
Munro, Jason M.
Griffin, Sinéad M.
Persson, Kristin A.
Shenoy, Vivek B.
High-throughput search for magnetic and topological order in transition metal oxides
title High-throughput search for magnetic and topological order in transition metal oxides
title_full High-throughput search for magnetic and topological order in transition metal oxides
title_fullStr High-throughput search for magnetic and topological order in transition metal oxides
title_full_unstemmed High-throughput search for magnetic and topological order in transition metal oxides
title_short High-throughput search for magnetic and topological order in transition metal oxides
title_sort high-throughput search for magnetic and topological order in transition metal oxides
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725452/
https://www.ncbi.nlm.nih.gov/pubmed/33298446
http://dx.doi.org/10.1126/sciadv.abd1076
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