Z2Pack: Numerical implementation of hybrid Wannier centers for identifying topological materials

Gresch, Dominik; Autès, G.; Yazyev, Oleg V.; Troyer, Matthias; Vanderbilt, David; Bernevig, B. Andrei; Soluyanov, Alexey A.

doi:10.1103/physrevb.95.075146

Cited by 407 publications

(258 citation statements)

References 160 publications

(280 reference statements)

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“…PAOFLOW can also calculate SHC, AHC, Fermi surfaces, topological invariants, and other properties. Topological invariants are also calculated in DFT using Z2PACK [78] and WANNIER TOOLS [79] which are integrated into many different DFT codes. Investigation of ballistic transport phenomena is possible via SIESTA-based codes [80], namely the TRANSIESTA [81], TRANSAMPA [82], and SMEAGOL [83] packages.…”

Section: Current Statusmentioning

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: Current Statusmentioning

confidence: 99%

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

et al. 2019

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“…The numerical calculations were performed using VASP [28,29]. The binding energies of adatoms (A), which are crucial for the strength of interaction between bismuthene and adatom, are calculated using the expression To test whether the bare and defected free-standing SL bismuthenes are topologically trivial or nontrivial, we carried out calculations with a WANNIER90 package and Z 2 -PACK [31,32], using ab initio VASP output. Z 2 invariant is calculated by tracking the time evolution of the hybrid Wannier charge centers (WCC) on the surface where the momentum, k y , in half BZ is mapped to a time interval (0,T /2), and k x is used to construct the maximally localized Wannier functions.…”

Section: Atomistic Model and Computational Detailsmentioning

confidence: 99%

Modification of electronic structure, magnetic structure, and topological phase of bismuthene by point defects

Kadıoğlu¹,

Kilic²,

Demirci³

et al. 2017

Phys. Rev. B

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This paper reveals how the electronic structure, magnetic structure, and topological phase of two-dimensional (2D), single-layer structures of bismuth are modified by point defects. We first showed that a free-standing, single-layer, hexagonal structure of bismuth, named h-bismuthene, exhibits nontrivial band topology. We then investigated interactions between single foreign adatoms and bismuthene structures, which comprise stability, bonding, electronic structure, and magnetic structures. Localized states in diverse locations of the band gap and resonant states in band continua of bismuthene are induced upon the adsorption of different adatoms, which modify electronic and magnetic properties. Specific adatoms result in reconstruction around the adsorption site. Single vacancies and divacancies can form readily in bismuthene structures and remain stable at high temperatures. Through rebondings, Stone-Whales-type defects are constructed by divacancies, which transform into a large hole at high temperature. Like adsorbed adatoms, vacancies induce also localized gap states, which can be eliminated through rebondings in divacancies. We also showed that not only the optical and magnetic properties, but also the topological features of pristine h-bismuthene can be modified by point defects. The modification of the topological features depends on the energies of localized states and also on the strength of coupling between point defects.

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“…The Chern number is calculated, in contrast to Eq. , as the integral over the Berry curvature (3) on the small sphere S around the Weyl point :

C = \frac{1}{2 π} \int_{normalS} d boldS \cdot Ω true(boldk true) = \pm 1 .

…”

Section: Introduction: Topology Meets Spinmentioning

confidence: 99%

Route towards Dirac and Weyl antiferromagnetic spintronics

Šmejkal

Jungwirth

Sinova

2017

Physica Rapid Research Ltrs

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Topological quantum matter and spintronics research have been developed to a large extent independently. In this review, we discuss a new role that the antiferromagnetic order has taken in combining topological matter and spintronics. This occurs due to the complex microscopic symmetries present in antiferromagnets that allow for, e.g., topological relativistic quasiparticles and the newly discovered N eel spin-orbit torques to coexist. We first introduce the concepts of topological semimetals and spin-orbitronics. Secondly, we explain the antiferromagnetic symmetries on a minimal Dirac semimetal model and the guiding role of ab initio calculations in predictions of examples of Dirac and Weyl antiferromagnets: SrMnBi 2 , CuMnAs, and Mn 3 Ge. Lastly, we illustrate the interplay of Dirac quasiparticles, topology and antiferromagnetism on: (i) the experimentally observed quantum Hall effect in EuMnBi 2 ; (ii) the large anomalous Hall effect in Mn 3 Ge; and (iii) the theoretically predicted topological metal-insulator transition in CuMnAs. Dirac fermions at the Fermi level of the Dirac semimetal antiferromagnet calculated from the fist-principles. Reorientation of the N eel vector drives the topological metal-insulator transition.

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Z2Pack: Numerical implementation of hybrid Wannier centers for identifying topological materials

Cited by 407 publications

References 160 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

Modification of electronic structure, magnetic structure, and topological phase of bismuthene by point defects

Route towards Dirac and Weyl antiferromagnetic spintronics

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