Non-Hermitian semi-Dirac semi-metals

Banerjee, Ayan; Narayan, Awadhesh

doi:10.48550/arxiv.2001.11188

Cited by 4 publications

(7 citation statements)

References 72 publications

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“…This rapidly burgeoning field encompasses both theoretical and experimental advancements in atomic physics [5,6], quantum optics [7,8], microwave cavities [9,10], and topological laser systems [11][12][13], using controlled dissipation with incorporation of gain and loss terms in open systems. Considering the interplay between non-Hermiticity and topology, several varieties of non-Hermitian topological semimetals [14] have been proposed, including knot [15], nodal line [16,17], nodal ring [16], Hopf link [18], Dirac [19,20], Weyl [5], and semi-Dirac [21], to name just a few. Some of these have been experimentally realized recently.…”

Section: Introductionmentioning

confidence: 99%

Controlling exceptional points with light

Banerjee

Narayan

2020

Phys. Rev. B

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We propose and show that application of light leads to an intriguing platform for controlling exceptional points in non-Hermitian topological systems. We demonstrate our proposal using three different non-Hermitian systems-nodal line semimetals, semi-Dirac semimetals, and Dirac semimetals-and show that using illumination with light one can engineer the positions and stability of exceptional points. We illustrate the topological properties of these models and map out their light-driven topological phase transitions.

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Section: Introductionmentioning

confidence: 99%

Controlling exceptional points with light

Banerjee

Narayan

2020

Phys. Rev. B

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“…The critical phase, known as the semi-Dirac phase, is semi-metallic with electrons dispersing linearly in one direction and quadratically in the other [2][3][4][5][6][7][8][9][10]. Examples of systems that exhibit such phases in two dimensions include TiO 2 /VO 2 heterostructures [11], (BEDT-TTF) 2 I 3 organic salts under pressure [12], photonic metamaterials [13] and certain non-Hermitian systems [14]. Semi-Dirac phases reveal distinct features, for example, in transport phenomena [15] or driven by light [16,17].…”

Section: Introductionmentioning

confidence: 99%

Quantum Hall effective action for anisotropic Dirac semi-metal

Hoyos,

Lier,

Peña-Benitez

et al. 2020

Preprint

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We present a study of Hall transport in semi-Dirac critical phases. The construction is based on a covariant formulation of relativistic systems with spatial anisotropy. Geometric data together with external electromagnetic fields is used to devise an expansion procedure that leads to a lowenergy effective action consistent with the discrete P T symmetry that we impose. We use the action to discuss terms contributing the Hall transport and extract the coefficients. We also discuss the associated scaling symmetry.

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“…For a three-dimensional non-Hermitian nodal line semimetal model, we constructed and trained a convolutional network to yield excellent accuracy in predictions of the topologcial phases. Our proposed methods could be potentially useful for machine learning of other non-Hermitian topological phases [52][53][54][55][56][57][58][59], including those with disorder [60,61]. Furthermore, we envisage that our methods could be applied for identification of non-Hermitian topological phases in future experiments.…”

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confidence: 99%

Machine learning non-Hermitian topological phases

Narayan

2021

Phys. Rev. B

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Non-Hermitian topological phases have gained widespread interest due to their unconventional properties, which have no Hermitian counterparts. In this work, we propose to use machine learning to identify and predict non-Hermitian topological phases, based on their winding number. We consider two examples -non-Hermitian Su-Schrieffer Heeger model in one dimension and non-Hermitian nodal line semimetal in three dimensions -to demonstrate the use of neural networks to accurately characterize the topological phases. We show that for the one dimensional model, a fully connected neural network gives an accuracy greater than 99.9%, and is robust to the introduction of disorder. For the three dimensional model, we find that a convolutional neural network accurately predicts the different topological phases.

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Non-Hermitian semi-Dirac semi-metals

Cited by 4 publications

References 72 publications

Controlling exceptional points with light

Controlling exceptional points with light

Quantum Hall effective action for anisotropic Dirac semi-metal

Machine learning non-Hermitian topological phases

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