Spin-Polarization Control Driven by a Rashba-Type Effect Breaking the Mirror Symmetry in Two-Dimensional Dual Topological Insulators

Acosta, Carlos Mera; Fazzio, A.

doi:10.1103/physrevlett.122.036401

Cited by 34 publications

(22 citation statements)

References 58 publications

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“…In the perfect scenario, one wishes to find an n-dimensional space defined by descriptors separating all fabricated materials into regions related to all topological and trivial insulators. Thus, systems characterized by more than one non-zero topological invariant [505], e.g., dual topological insulators, would be in the intersection of the regions describing different topological phases. In these 'materials maps', the boundary of such regions should then be related to the topological transitions.…”

Section: Topological Materials Classificationmentioning

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 Materials Classificationmentioning

confidence: 99%

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

et al. 2019

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“…A journey through other wavevectors in a BZ visits symmetries different than the macroscopic space group symmetries: For each CPGS, there exists another layer of symmetries of particular wavevectors 𝑘 ! (show also in Table I) in the corresponding Brillouin zone [27][28][29], which form subgroups of the CPGS and enable specific momentum and band dependent properties in the crystal such as band crossing, anti--crossing [30], topological band inversion [31][32][33], and topological protection [34]. The inspection to ST at other k--points revels STs that are not predicted by the CPGS.…”

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confidence: 93%

Different shapes of spin textures as a journey through the Brillouin zone

et al. 2021

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Crystallographic space group symmetry (CPGS) such as polar and nonpolar crystal classes has long been known to classify compounds that have spin--orbit--induced spin splitting such as Rashba and Dresselhaus compounds, respectively. However, the enabling symmetries underlying the spin texture prototypes ---the relationship between the expectation value of spin operator 𝑆 !" in Bloch state 𝑢(𝑛, 𝑘) and the wavevector 𝑘 - are instead specific rotations and reflection symmetries in the wavevector point group symmetry rather than the presence or absence of polar field in the global crystallographic space group. Relativistic band structures calculations demonstrate how compounds judged by their macroscopic CPGS to be typical Dresselhaus (GaAs) or Rashba (GeTe) or Wyle (Tellurium) compounds, manifest both Rashba and Dresselhaus spin textures, respectively at different wavevector chiralities and polarities.

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“…This symmetry-based approach could be conveniently applied to other topologically nontrivial two-dimensional materials such as antimonene and arsenene 40,41 . Another advantage of these simplified but accurate models is that they can be easily modified to incorporate external factors such as strain, electric and magnetic fields 42,43 . Moreover, these models can be a cornerstone for designing new topological structures exhibiting exotic quantum transport properties.…”

Section: Basis Functionsmentioning

confidence: 99%

Localized Wannier function based tight-binding models for two-dimensional allotropes of bismuth

Li,

Smith,

Yin

et al. 2021

Preprint

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Spin-Polarization Control Driven by a Rashba-Type Effect Breaking the Mirror Symmetry in Two-Dimensional Dual Topological Insulators

Cited by 34 publications

References 58 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

Different shapes of spin textures as a journey through the Brillouin zone

Localized Wannier function based tight-binding models for two-dimensional allotropes of bismuth

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