Proximity spin-orbit and exchange coupling in ABA and ABC trilayer graphene van der Waals heterostructures

Zollner, Klaus; Gmitra, Martin; Fabian, Jaroslav

doi:10.1103/physrevb.105.115126

Cited by 23 publications

(8 citation statements)

References 133 publications

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“…The stability of different stacking configurations in TLG is influenced by several factors including interlayer interactions, lattice symmetry, and energy minimization . ABA stacking is known to be a more stable configuration , compared to ABC stacking due to the favorable alignment of the carbon atoms and the strong interlayer interactions between them. , The ABC stacking obtained by inducing the stacking order change is considered a metastable configuration due to its higher TE/atom.…”

Section: Resultsmentioning

confidence: 99%

Uniaxial Strain-Induced Stacking Order Change in Trilayer Graphene

Dey,

Azizimanesh,

et al. 2024

ACS Appl. Mater. Interfaces

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The layer stacking order in two-dimensional heterostructures, like graphene, affects their physical properties and potential applications. Trilayer graphene, specifically ABCtrilayer graphene, has captured significant interest due to its potential for correlated electronic states. However, achieving a stable ABC arrangement is challenging due to its lower thermodynamic stability compared to the more stable ABA stacking. Despite recent advancements in obtaining ABC graphene through external perturbations, such as strain, the stacking transition mechanism remains insufficiently explored. In this study, we unveil a universal mechanism to achieve ABC stacking, applicable for understanding ABA to ABC stacking changes induced by any mechanical perturbations. Our approach is based on a novel strain engineering technique that induces interlayer slippage and results in the formation of stable ABC domains. We investigate the underlying interfacial mechanisms of this stacking change through computational simulations and experiments. Our findings demonstrate a highly anisotropic and significant transformation of ABA stacking to large and stable ABC domains facilitated by interlayer slippage. Through atomistic simulations and local energy analysis, we systematically demonstrate the mechanism for this stacking transition, that is dependent on specific loading orientation. Understanding such a mechanism allows this material system to be engineered by design compatible with industrial techniques on a device-by-device level. We conduct Raman studies to validate and characterize the formed ABC stacking, highlighting its distinct features compared to the ABA region. Our results contribute to a clearer understanding of the stacking change mechanism and provide a robust and controllable method for achieving stable ABC domains, facilitating their use in developing advanced optoelectronic devices.

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Section: Resultsmentioning

confidence: 99%

Uniaxial Strain-Induced Stacking Order Change in Trilayer Graphene

Dey,

Azizimanesh,

et al. 2024

ACS Appl. Mater. Interfaces

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“…We consider an artificial two-dimensional superconductor obtained by combining a two-dimensional ferromagnet and a two-dimensional superconductor [32][33][34] as shown in figure 1(a). The electronic structure of the heterostructure is modeled with an atomistic Wannier orbital site forming a triangular lattice, where the moiré pattern is incorporated in the modulation of the Hamiltonian parameters [60][61][62][63][64]. The full Hamiltonian takes the form…”

Section: Modelmentioning

confidence: 99%

Hamiltonian learning with real-space impurity tomography in topological moiré superconductors

Khosravian,

Koch,

Lado

2024

J. Phys. Mater.

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Extracting Hamiltonian parameters from available experimental data is a challenge in quantum materials. In particular, real-space spectroscopy methods such as scanning tunneling spectroscopy allow probing electronic states with atomic resolution, yet even in those instances extracting the effective Hamiltonian is an open challenge. Here we show that impurity states in modulated systems provide a promising approach to extracting non-trivial Hamiltonian parameters of a quantum material. We show that by combining the real-space spectroscopy of different impurity locations in a moiré topological superconductor, modulations of exchange and superconducting parameters can be inferred via machine learning. We demonstrate our strategy with a physically-inspired harmonic expansion combined with a fully-connected neural network that we benchmark against a conventional convolutional architecture. We show that while both approaches allow extracting exchange modulations, only the former approach allows inferring the features of the superconducting order. Our results demonstrate the potential of machine learning methods to extract Hamiltonian parameters by real-space impurity spectroscopy as local probes of a topological state.

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“…The goal is to find an effective description for the low-energy physics, which is relevant for studying transport [23,24,26,45,94], topology [52,95], spin relaxation [38,43,96,97], or emergent long-range or-der [98]. Due to the short-range nature of the proximity effects in van der Waals heterostructures, the effective models are transferable [99][100][101]. For example, a bilayer-graphene encapsulated from two sides by different materials can be described by combining the effective models for two proximitized graphene sheets, coupled by bilayer-graphene interlayer couplings [99,100].…”

Section: Low Energy Model Hamiltonianmentioning

confidence: 99%