High-throughput first-principle prediction of collinear magnetic topological materials

doi:10.1038/s41524-022-00954-w

	High-throughput first-principle prediction of collinear magnetic topological materials
	Su, Yunlong1 ; Hu, Jiayu1 ; Cai, Xiaochan1 ; Shi, Wujun2,3 ; Xia, Yunyouyou1,4 ; Xu, Yuanfeng 5,6,7; Xu, Xuguang1 ; Chen, Yulin1,4,8 ; Li, Gang1,4
	2022-12-27
发表期刊	NPJ COMPUTATIONAL MATERIALS
EISSN	2057-3960
卷号	8 期号:1
发表状态	已发表
DOI	10.1038/s41524-022-00954-w
摘要	The success of topological band theory and symmetry-based topological classification significantly advances our understanding of the Berry phase. Based on the critical concept of topological obstruction, efficient theoretical frameworks, including topological quantum chemistry and symmetry indicator theory, were developed, making a massive characterization of real materials possible. However, the classification of magnetic materials often involves the complexity of their unknown magnetic structures, which are often hard to know from experiments, thus, hindering the topological classification. In this paper, we design a high-throughput workflow to classify magnetic topological materials by automating the search for collinear magnetic structures and the characterization of their topological natures. We computed 1049 chosen transition-metal compounds (TMCs) without oxygen and identified 64 topological insulators and 53 semimetals, which become 73 and 26 when U correction is further considered. Due to the lack of magnetic structure information from experiments, our high-throughput predictions provide insightful reference results and make the step toward a complete diagnosis of magnetic topological materials.
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收录类别	SCI ; EI
语种	英语
资助项目	Shanghai Technology Innovation Action Plan 2020-Integrated Circuit Technology Support Program[20DZ1100605] ; National Natural Science Foundation of China[11874263] ; Sino-German mobility program[M-0006] ; National Key R&D Program of China[2017YFE0131300] ; Science and Technology Commission of Shanghai Municipality (STCSM)[22ZR1441800] ; Shanghai-XFEL Beamline Project[31011505505885920161A2101001]
WOS研究方向	Chemistry ; Materials Science
WOS类目	Chemistry, Physical ; Materials Science, Multidisciplinary
WOS记录号	WOS:000905042400001
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/268671
专题	物质科学与技术学院_博士生物质科学与技术学院物质科学与技术学院_PI研究组_李刚组物质科学与技术学院_特聘教授组_陈宇林物质科学与技术学院_硕士生物质科学与技术学院_公共科研平台_拓扑物理实验室大科学中心_公共科研平台_大科学装置建设部
通讯作者	Li, Gang
作者单位	1.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China 2.ShanghaiTech Univ, Ctr Transformat Sci, Shanghai 201210, Peoples R China 3.ShanghaiTech Univ, Shanghai High Repetit Rate XFEL & Extreme Light Fa, Shanghai 201210, Peoples R China 4.ShanghaiTech Univ, ShanghaiTech Lab Topol Phys, Shanghai 201210, Peoples R China 5.Zhejiang Univ, Ctr Correlated Matter, Hangzhou 310058, Peoples R China 6.Zhejiang Univ, Sch Phys, Hangzhou 310058, Peoples R China 7.Princeton Univ, Dept Phys, Princeton, NJ USA 8.Univ Oxford, Dept Phys, Clarendon Lab, Parks Rd, Oxford OX1 3PU, England
第一作者单位	物质科学与技术学院
通讯作者单位	物质科学与技术学院; 上海科技大学
第一作者的第一单位	物质科学与技术学院
推荐引用方式 GB/T 7714	Su, Yunlong,Hu, Jiayu,Cai, Xiaochan,et al. High-throughput first-principle prediction of collinear magnetic topological materials[J]. NPJ COMPUTATIONAL MATERIALS,2022,8(1).
APA	Su, Yunlong.,Hu, Jiayu.,Cai, Xiaochan.,Shi, Wujun.,Xia, Yunyouyou.,...&Li, Gang.(2022).High-throughput first-principle prediction of collinear magnetic topological materials.NPJ COMPUTATIONAL MATERIALS,8(1).
MLA	Su, Yunlong,et al."High-throughput first-principle prediction of collinear magnetic topological materials".NPJ COMPUTATIONAL MATERIALS 8.1(2022).