Converting normal insulators into topological insulators via tuning orbital levels

Shi, Wujun; Liu, Junwei; Xu, Yong; Xiong, Shi‐Jie; Wu, Jian; Duan, Wenhui

doi:10.1103/physrevb.92.205118

Cited by 22 publications

(17 citation statements)

References 60 publications

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“…These systems are in turn characterized by a non-zero topological invariant Z 2 where the sum is only over the occupied states ( < n E f ). The topological phase can thus be changed by modifying the orbital character of the occupied states [484] or breaking the symmetry that protects the topological phase. Consequently, quantum phase transitions from trivial insulators (non-topological insulators) to topological insulators can be induced by external perturbations.…”

Section: Topological Ordered Materialsmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Topological Ordered Materialsmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…The 3D TIs materials include Bi 1−x Sb x alloys 9,23 , Bi 2 Se 3 -class materials [24][25][26] , half-Heusler compounds 27,28 , TlBiSe 2 family chalcogenides [29][30][31] , strained HgTe 9,32 , α-Sn 9,33 , and bismuth-based III-V semiconductors 34 , etc. [35][36][37] Additional ways could convert the normal insulators into TIs, such as external strain 38 , chemical doping 39 .…”

Section: In the Past Decade A New Field Dubbed Topological Insulatormentioning

confidence: 99%

Electronic properties of SnTe-class topological crystalline insulator materials

Wang

Huang

et al. 2016

Chinese Phys. B

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The rise of topological insulators in recent years has broken new ground both in the conceptual cognition of condensed matter physics and the promising revolution of the electronic devices. It also stimulates the explorations of more topological states of matter. Topological crystalline insulator is a new topological phase, which combines the electronic topology and crystal symmetry together. In this article, we review the recent progress in the studies of SnTe-class topological crystalline insulator materials. Starting from the topological identifications in the aspects of the bulk topology, surface states calculations and experimental observations, we present the electronic properties of topological crystalline insulators under various perturbations, including native defect, chemical doping, strain, and thickness-dependent confinement effects, and then discuss their unique quantum transport properties, such as valley-selective filtering and helicity-resolved functionalities for Dirac fermions. The rich properties and high tunability make SnTe-class materials promising candidates for novel quantum devices.

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“…Halide perovskites, enriched with structurally modulated diverse electronic phases, are promising materials for research in the fundamental area of band topology and in the applied area of optoelectronics [1][2][3][4][5]. These compounds with the formula ABX 3 (A is either organic or inorganic, B = Sn, Pb or Ge and X = Cl, Br or I) are structurally flexible to external stimuli such as pressure, temperature and chemical doping [6][7][8][9][10][11]. The symmetry altering structural modifications invite changes in the interplay of lattice, orbital and spin degrees of freedom to manifest trivial and non-trivial electronic phases.…”

Section: Introductionmentioning

confidence: 99%

Defining the topological influencers and predictive principles to engineer the band structure of halide perovskites

2020

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Complex quantum coupling phenomena of halide perovskites are examined through ab-initio calculations and exact diagonalization of model Hamiltonians to formulate a set of fundamental guiding rules to engineer the bandgap through strain. The bandgap tuning in halides is crucial for photovoltaic applications and for establishing non-trivial electronic states. Using CsSnI3 as the prototype material, we show that in the cubic phase, the bandgap reduces irrespective of the nature of strain. However, for the tetragonal phase, it reduces with tensile strain and increases with compressive strain, while the reverse is the case for the orthorhombic phase. The reduction can give rise to negative bandgap in the cubic and tetragonal phases leading to normal to topological insulator phase transition. Also, these halides tend to form a stability plateau in a space spanned by strain and octahedral rotation. In this plateau, with negligible cost to the total energy, the bandgap can be varied in a range of 1eV. Furthermore, we present a descriptor model for the perovskite to simulate their bandgap with strain and rotation. Analysis of band topology through model Hamiltonians led to the conceptualization of topological influencers that provide a quantitative measure of the contribution of each chemical bonding towards establishing a normal or topological insulator phase. On the technical aspect, we show that a four orbital based basis set (Sn-{s,p} for CsSnI3) is sufficient to construct the model Hamiltonian which can explain the electronic structure of each polymorph of halide perovskites.

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Converting normal insulators into topological insulators via tuning orbital levels

Cited by 22 publications

References 60 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

Electronic properties of SnTe-class topological crystalline insulator materials

Defining the topological influencers and predictive principles to engineer the band structure of halide perovskites

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