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

被引:52
作者
Frey, Nathan C. [1 ,2 ]
Horton, Matthew K. [2 ,3 ]
Munro, Jason M. [2 ]
Griffin, Sinead M. [4 ,5 ]
Persson, Kristin A. [2 ,3 ]
Shenoy, Vivek B. [1 ]
机构
[1] Univ Penn, Dept Mat Sci & Engn, Philadelphia, PA 19104 USA
[2] Lawrence Berkeley Natl Lab, Energy Technol Area, Berkeley, CA 94720 USA
[3] Univ Calif Berkeley, Dept Mat Sci & Engn, Berkeley, CA 94704 USA
[4] Lawrence Berkeley Natl Lab, Mol Foundry, Berkeley, CA 94720 USA
[5] Lawrence Berkeley Natl Lab, Mat Sci Div, Berkeley, CA 94720 USA
关键词
CRYSTAL-STRUCTURE; INSULATOR; CATALOG; ROBUST;
D O I
10.1126/sciadv.abd1076
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:9
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