Hidden magnetism and split off flat bands in the insulator metal transition in VO2

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-09-13 DOI:10.1038/s41524-024-01382-8
Xiuwen Zhang, Jia-Xin Xiong, Alex Zunger
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Abstract

Transition metal d-electron oxides with an odd number of electrons per unit cell are expected to form metals with partially occupied energy bands, but exhibit in fact a range of behaviors, being either insulators, or metals, or having insulator-metal transitions. Traditional explanations involved predominantly electron-electron interactions in fixed structural symmetry. The present work focuses instead on the role of symmetry breaking local structural motifs. Viewing the previously observed V-V dimerization in VO2 as a continuous knob, reveals in density functional calculations the splitting of an isolated flat band from the broad conduction band. This leads past a critical percent dimerization to the formation of the insulating phase while lowering the total energy. In VO2 this transition is found to have a rather low energy barrier approaching the thermal energy at room temperature, suggesting energy-efficient switching in neuromorphic computing. Interestingly, sufficient V-V dimerization suppresses magnetism, leading to the nonmagnetic insulating state, whereas magnetism appears when dimerization is reduced, forming a metallic state. This study opens the way to design novel functional quantum materials with symmetry breaking-induced flat bands.

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VO2 中绝缘体金属转变过程中的隐磁性和分裂平带
每个单胞电子数为奇数的过渡金属 d 电子氧化物预计会形成具有部分占据能带的金属,但实际上却表现出一系列行为,要么是绝缘体,要么是金属,要么具有绝缘体-金属的转变。传统的解释主要涉及固定结构对称中的电子-电子相互作用。而本研究则侧重于打破对称性的局部结构图案的作用。将之前观察到的二氧化钛中的 V-V 二聚化视为一个连续的旋钮,密度泛函计算揭示了从宽泛的导带中分裂出一个孤立的平带。这导致二聚化超过临界百分比,形成绝缘相,同时降低了总能量。在二氧化钛中,这一转变的能量势垒相当低,接近室温下的热能,这表明在神经形态计算中可以实现高能效的转换。有趣的是,充分的 V-V 二聚化会抑制磁性,导致非磁性绝缘态,而当二聚化减少时,磁性就会出现,形成金属态。这项研究为设计具有对称性破缺诱导平带的新型功能量子材料开辟了道路。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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