Discovering two-dimensional topological insulators from high-throughput computations

Olsen, Thomas; Andersen, Erik Bahl; Okugawa, Takuya; Torelli, Daniele; Deilmann, Thorsten; Thygesen, Kristian Sommer

doi:10.1103/physrevmaterials.3.024005

Cited by 81 publications

(61 citation statements)

References 65 publications

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“…The largest gap found was 48 meV for TiNiI. Olsen et al discovered several 2D nontrivial materials including topological insulators, topological crystalline insulators, quantum anomalous Hall insulators and dual topological insulators (which possess time reversal and mirror symmetry) [381]. The 3D databases are well established and have been used widely.…”

Section: D 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: D Materialsmentioning

confidence: 99%

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

et al. 2019

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“…We choose here to focus on Bismuth and Antimony halides, i.e. binary compounds BiX and SbX with X = (F, Cl, Br, I), whose topological nature has already been investigated in earlier work 28,31,32 . The reason for our choice is threefold:…”

Section: Electronic Structure Of Topological Nanoribbonsmentioning

confidence: 99%

Conductance of quantum spin Hall edge states from first principles: The critical role of magnetic impurities and inter-edge scattering

2020

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The outstanding transport properties expected at the edge of two-dimensional time-reversal invariant topological insulators have proven to be challenging to realize experimentally, and have so far only been demonstrated in very short devices. In search for an explanation to this puzzling observation, we here report a full first-principles calculation of topologically protected transport at the edge of novel quantum spin Hall insulators -specifically, Bismuth and Antimony halidesbased on the non-equilibrium Green's functions formalism. Our calculations unravel two different scattering mechanisms that may affect two-dimensional topological insulators, namely time-reversal symmetry breaking at vacancy defects and inter-edge scattering mediated by multiple co-operating impurities, possibly non-magnetic. We discuss their drastic consequences for typical non-local transport measurements as well as strategies to mitigate their negative impact. Finally, we provide an instructive comparison of the transport properties of topologically protected edge states to those of the trivial edge states in MoS2 ribbons. Although we focus on a few specific cases (in terms of materials and defect types) our results should be representative for the general case and thus have significance beyond the systems studied here.

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“…In this study, we propose an alternative strategy to realize the QAH effect in 2D magnetic van der Waals (VdW) heterojunctions by combining data-driven discovery of 2D magnetic compounds with a theoretical analysis of topological properties. All the previous works [13][14][15][16]25,26 on the QAH heterojunctions require at least one intrinsic topological material. In contrast, every QAH VdW heterojunction predicted by our approach can be constructed with two trivial 2D ferromagnetic (FM) insulators.…”

Section: Discovery Workflow For Qah Heterojunctionsmentioning

confidence: 99%

“…In the past decade, a tremendous amount of computed and experimental material data has been shared in the materials research community through multiple material databases for both bulk [34][35][36] and 2D inorganic compounds [37][38][39][40][41] . This offered a vast inorganic materials space that is explorable by data-driven approaches and enabled the data-driven discovery of quantum materials hosting exotic topological phases 25,26,[42][43][44] . Here, our theoretical predictions of 2D heterojunctions that host the QAH effect is motivated by the data-driven discovery of a family of 2D monolayer MXY compounds (M = metal atoms, X = N, S, Se, Te, Y = F, Cl, Br, I) that are predicted to host both FM and nonmetallic ground states.…”

Section: Prediction Of 2d Magnetic Insulators In Mxy Compoundsmentioning

confidence: 99%

Quantum anomalous Hall effect in two-dimensional magnetic insulator heterojunctions

Pan¹,

Yu²,

Zhang³

et al. 2020

npj Comput Mater

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Recent years have witnessed tremendous success in the discovery of topological states of matter. Particularly, sophisticated theoretical methods in time-reversal-invariant topological phases have been developed, leading to the comprehensive search of crystal database and the prediction of thousands of topological materials. In contrast, the discovery of magnetic topological phases that break time reversal is still limited to several exemplary materials because the coexistence of magnetism and topological electronic band structure is rare in a single compound. To overcome this challenge, we propose an alternative approach to realize the quantum anomalous Hall (QAH) effect, a typical example of magnetic topological phase, via engineering two-dimensional (2D) magnetic van der Waals heterojunctions. Instead of a single magnetic topological material, we search for the combinations of two 2D (typically trivial) magnetic insulator compounds with specific band alignment so that they can together form a type-III broken-gap heterojunction with topologically non-trivial band structure. By combining the data-driven materials search, first-principles calculations, and the symmetry-based analytical models, we identify eight type-III broken-gap heterojunctions consisting of 2D ferromagnetic insulators in the MXY compound family as a set of candidates for the QAH effect. In particular, we directly calculate the topological invariant (Chern number) and chiral edge states in the MnNF/MnNCl heterojunction with ferromagnetic stacking. This work illustrates how data-driven material science can be combined with symmetry-based physical principles to guide the search for heterojunction-based quantum materials hosting the QAH effect and other exotic quantum states in general.

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Discovering two-dimensional topological insulators from high-throughput computations

Cited by 81 publications

References 65 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

Conductance of quantum spin Hall edge states from first principles: The critical role of magnetic impurities and inter-edge scattering

Quantum anomalous Hall effect in two-dimensional magnetic insulator heterojunctions

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