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We theoretically investigate the RKKY interaction in helical higher-order topological insulators (HOTIs), revealing distinct behaviors mediated by hinge and Dirac-type bulk carriers. Our findings show that hinge-mediated interactions consist of Heisenberg, Ising, and Dzyaloshinskii-Moriya (DM) terms, exhibiting a decay with impurity spacing z and oscillations with Fermi energy {\epsilon}F . These interactions demonstrate ferromagnetic behaviors for the Heisenberg and Ising terms and alternating behavior for the DM term. In contrast, bulk-mediated interactions include Heisenberg, twisted Ising, and DM terms, with a conventional cubic oscillating decay. This study highlights the nuanced interplay between hinge and bulk RKKY interactions in HOTIs, offering insights into the design of next-generation quantum devices based on the HOTIs., Comment: 8 pages, 5 figures

Superconducting-proximity topological insulators (STIs) have garnered significant research attention over the past two decades. In this Letter, we demonstrate that a low-dimensional STI in the topological-nontrivial phase (TP) exhibits an interference-induced surface (boundary) superconductivity with the surface critical temperature $T_{cs}$ significantly higher than the bulk one $T_{cb}$. Such a surface superconductivity is built due to the interference of the scattering quasiparticle states, rather than due to the presence of the topological bound states (TBSs). As the system goes deeper into the TP, the surface exhibits a crossover from the interference- to TBS-induced phase, where the surface enhancement of superconductivity is governed by the TBSs. Our study unveils a substantial variation in the maximal $T_{cs}$ along this crossover, attaining values being twice the maximal bulk critical temperature of the STI. Beyond shedding light on the nature of surface superconductivity in STIs, our study introduces a tangible method for experimentally manipulating their critical superconducting temperatures., Comment: 6 pages, 4 figures

We investigate theoretically tunable non-Hermitian skin effect in systems with gain and loss, and find that bipolar (quadripolar) non-Hermitian skin effect characterized by topological invariants in one (two)-dimensional system. We also find the partial non-Hermitian skin effect with the coexistence of localized states and extended states. Both types of the non-Hermitian skin effect have not yet been predicted together in a single system. A feasible experimental scheme of our model is proposed to realize in electric circuits. Our investigation unveils a new type of non-Hermitian skin effect and enhance the tunability of the non-Hermitian systems by gain and loss other than the conventional non-reciprocal hopping., Comment: 7 pages, 6 figures

Electronic band structures underlie the physical properties of crystalline materials, their geometrical exploration renovates the conventional cognition and brings about novel applications. Inspired by geometry phases, we introduce a geometric amplitude named as the geometric density of states (GDOS) dictated by the differential curvature of the constant-energy contour. The GDOS determines the amplitude of the real-space Green's function making it attain the ultimate expression with transparent physics. The local responses of crystalline materials are usually formulated by the real-space Green's function, so the relevant physics should be refreshed by GDOS. As an example of local responses, we suggest using scanning tunneling microscopy (STM) to characterize the surface states of three-dimensional topological insulator under an in-plane magnetic field. The GDOS favors the straightforward simulation of STM measurement without resorting to Fourier transform of the real-space measurement, and also excavates the unexplored potential of STM measurement to extract the phase information of wavefunction through its amplitude, i.e., the spin and curvature textures. Therefore, the proposed GDOS deepens the understanding of electronic band structures and is indispensable in local responses, and it should be universal for any periodic systems., Comment: 6 pages, 2 figures

The ferromagnetic and antiferromagnetic nanostructure are crucial for fundamental spintronics devices, motivated by its potential application in spintronics, we theoretically investigate the electronic structure of the ferromagnetic and antiferromagnetic phases of the cylindrical intrinsically magnetic topological insulator $\mathrm{MnBi_{2}Te_{4}}$ quantum wires for both cases. We demonstrate that a few surface states exist between the bulk band gap in the ferromagnetic phase, with only one spin branch. In the antiferromagnetic phase, we show that three coexistent states exist between the energy gaps of the quantum wires.

Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose the physical insight of multi-scale information about materials. And the process of labeling data consumes many resources and inevitably introduces errors, which constrains the accuracy of prediction. We propose to train the GNN model by self-supervised learning on the node and edge information of the crystal graph. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can reach better prediction performance on material properties. Furthermore, it may provide physical insights by tuning the range information. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. To incorporate rich physical information into the GNN model, we develop the node embedding graph neural networks (NEGNN) framework and show significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy., Comment: 19 pages, 12 figures, 8 tables

Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications. It is highly demanded of the accurate and efficient theoretical method for determining the critical transition temperatures, N\'{e}el temperatures, of antiferromagnetic materials. The powerful graph neural networks (GNN) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNN on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us obtain a more accurate and efficient model for N\'{e}el temperatures, and the trained model can successfully predict high N\'{e}el temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties., Comment: 7 pages, 6 figures, 2 tables

The tunneling of electrons and holes in quantum structures plays a crucial role in studying the transport properties of materials and the related devices. 8-Pmmn borophene is a new two-dimensional Dirac material, which hosts tilted Dirac cone and chiral, anisotropic massless Dirac fermions. We develop the transfer matrix method to investigate the Klein tunneling of massless fermions across the smooth NP junctions and NPN junctions of 8-Pmmn borophene. Like the sharp NP junctions of 8-Pmmn borophene, the tilted Dirac cones induce the oblique Klein tunneling. The angle of perfect transmission to the normal incidence is 20.4 degrees, a constant determined by the Hamiltonian of 8-Pmmn borophene. For the NPN junction, there are branches of the Klein tunneling in the phase diagram. We find that the asymmetric Klein tunneling is induced by the chirality and anisotropy of the carriers. Furthermore, we show the oscillation of electrical resistance related to the Klein tunneling in the NPN junctions. One may analyze the pattern of electrical resistance and verify the existence of asymmetric Klein tunneling experimentally., Comment: 7 pages, 8 figures

We present a theory of both the itinerant carrier-mediated RKKY interaction and the virtual excitations-mediated Bloembergen-Rowland (BR) interaction between magnetic moments in graphene induced by proximity effect with a ferromagnetic film. We show that the RKKY/BR interaction consists of the Heisenberg, Ising, and Dzyaloshinskii-Moriya (DM) terms. In the case of the nearest distance, we estimate the DM term from the RKKY/BR interaction is about 0.13 meV for the graphene/Co interface, which is consistent with the experimental result of DM interaction $0.16 \pm 0.05$ meV. Our calculations indicate that the intralayer RKKY/BR interaction may be a possible physical origin of the DM interaction in the graphene-ferromagnet interface. This work provides a new perspective to comprehend the DM interaction in graphene/ferromagnet systems., Comment: 5 pages, 3 figures

We investigate theoretically the phase transition in three dimensional cubic Ising model utilizing state-of-the-art machine learning algorithms. Supervised machine learning models show high accuracies (~99\%) in phase classification and very small relative errors ($< 10^{-4}$) of the energies in different spin configurations. Unsupervised machine learning models are introduced to study the spin configuration reconstructions and reductions, and the phases of reconstructed spin configurations can be accurately classified by a linear logistic algorithm. Based on the comparison between various machine learning models, we develop a few-shot strategy to predict phase transitions in larger lattices from trained sample in smaller lattices. The few-shot machine learning strategy for three dimensional(3D) Ising model enable us to study 3D ising model efficiently and provides a new integrated and highly accurate approach to other spin models.

We propose a physical mechanism to generate and selectively amplify anisotropic Rudermann-Kittel-Kasuya-Yosida (RKKY) interactions between two local spins. The idea is to combine the deflection of the carrier velocity by a P-N interface and the locking of this velocity to the carrier spin orientation via spin-orbit coupling. We provide analytical and numerical results to demonstrate this mechanism on the surface of a topological insulator P-N junction. This work identifies the P-N interface as a second knob which, together with the carrier density, enables independent control of the strength and anisotropy of the RKKY interaction over a wide range. These findings may be relevant to scalable quantum computation and two-impurity quantum criticality., Comment: 10 pages, 6 figures

The carrier-mediated RKKY interaction between local spins plays an important role for the application of magnetically doped graphene in spintronics and quantum computation. Previous studies largely concentrate on the influence of electronic states of uniform systems on the RKKY interaction. Here we reveal a very different way to manipulate the RKKY interaction by showing that the anomalous focusing - a well-known electron optics phenomenon in graphene P-N junctions - can be utilized to refocus the massless Dirac electrons emanating from one local spin to the other local spin. This gives rise to rich spatial interference patterns and symmetry-protected non-oscillatory RKKY interaction with a strongly enhanced magnitude. It may provide a new way to engineer the long-range spin-spin interaction in graphene., Comment: 9 pages, 4 figures

We demonstrate theoretically the coexistence of Dirac semimetal and topological insulator phases in InSb/$\alpha$-Sn conventional semiconductor superlattices, based on advanced first-principles calculations combined with low-energy $k\cdot p$ theory. By proper interfaces designing, a large interface polarization emerges when the growth direction is chosen along {[}111{]}. Such an intrinsic polarized electrostatic field reduces band gap largely and invert the band structure finally, leading to emerge of the topological Dirac semimetal phase with a pair of Dirac nodes appearing along the (111) crystallographic direction near the $\Gamma$ point. The surface states and Fermi arc are clearly observed in (100) projected surface. In addition, we also find a two-dimensional topological insulator phase with large nontrivial band gap approaching 70 meV, which make it possible to observe the quantum spin Hall effect at room temperature. Our proposal paves a way to realize topological nontrivial phases coexisted in conventional semiconductor superlattices by proper interface designing.

We predict a novel quantum interference based on the negative refraction across a semiconductor P-N junction: with a local pump on one side of the junction, the response of a local probe on the other side behaves as if the disturbance emanates not from the pump but instead from its mirror image about the junction. This phenomenon is guaranteed by translational invariance of the system and matching of Fermi surfaces of the constituent materials, thus it is robust against other details of the junction (e.g., junction width, potential profile, and even disorder). The recently fabricated P-N junctions in 2D semiconductors provide ideal platforms to explore this phenomenon and its applications to dramatically enhance charge and spin transport as well as carrier-mediated long-range correlation.

We study theoretically the RKKY interaction between magnetic impurities on the surface of three-dimensional topological insulators, mediated by the helical Dirac electrons. Exact analytical expression shows that the RKKY interaction consists of the Heisenberg-like, Ising-like and DM-like terms. It provides us a new way to control surface magnetism electrically. The gap opened by doped magnetic ions can lead to a short-range Bloembergen-Rowland interaction. The competition among the Heisenberg, Ising and DM terms leads to rich spin configurations and anomalous Hall effect on different lattices., Comment: 5 pages, 3 figures, 1 table

We demonstrate in theory that it is possible to all-electrically manipulate the RKKY interaction in a quasi-one-dimensional electron gas embedded in a semiconductor heterostructure, in the presence of Rashba and Dresselhaus spin-orbit interaction. In an undoped semiconductor quantum wire where intermediate excitations are gapped, the interaction becomes the short-ranged Bloembergen-Rowland super-exchange interaction. Owing to the interplay of different types of spin-orbit interaction, the interaction can be controlled to realize various spin models, e.g., isotropic and anisotropic Heisenberg-like models, Ising-like models with additional Dzyaloshinsky-Moriya terms, by tuning the external electric field and designing the crystallographic directions. Such controllable interaction forms a basis for quantum computing with localized spins and quantum matters in spin lattices., Comment: 5 pages, 1 figure