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Wenhui Duan

Publications and source records attributed to Wenhui Duan.

At least 19 recordsLinked to original sources
arXiv2026

Restoring the Surface Magnetic Gap in MnBi$_2$Te$_4$

A widespread experimental realization of quantized anomalous transport in the intrinsic magnetic topological insulator MnBi$_2$Te$_4$ is hindered by its elusive surface magnetic gap. Uncovering the origin of the gapless states is essential for accessing its topological properties. Here we show that surface defects lower the electrostatic potential, drive topological surface states into subsurface layers, suppress exchange interactions, thereby closing the gap. Tuning the surface electrostatic potential via external electric fields or interfacial fields in van der Waals heterostructures restores the expected gap and enables control of its topology. This is confirmed by model calculations and validated in defective MnBi$_2$Te$_4$ films interfaced with polar insulators, explaining the enhanced quantum anomalous Hall effect under AlO$_x$ capping observed in recent experiments. Our theory identifies electrostatically driven surface-state delocalization as a competitive origin of gap suppression and proposes displacement-field engineering for robust quantized transport.

cond-mat.mtrl-sci↗
arXiv2026

Spin-Chirality-Driven Bulk Photovoltaic Effect in van der Waals Magnet CrSBr

The bulk photovoltaic effect (BPVE) can be greatly enriched in magnetic materials. Here, we establish vector spin chirality as a tunable knob for generating an unconventional time-reversal-even magnetic BPVE, comprising the chiral shift current (CSC) and chiral injection current (CIC). Using bilayer antiferromagnetic (AFM) CrSBr as a prototype, we theoretically demonstrate the emergence of CSC and CIC. Compared with conventional photovoltaic currents arising from noncentrosymmetric crystal structures or collinear magnetic orderings, CSC and CIC not only possess comparable magnitudes but also exhibit exceptional tunability. Specifically, they can be switched on and off by magnetic-field-induced spin canting, reversed in direction upon canting-direction reversal, and continuously modulated in intensity via canting-angle variation. Furthermore, we reveal an unusual optical transition channel governing both currents in CrSBr. Our work establishes an unconventional magnetic BPVE with remarkable controllability, paving the way for applications in optoelectronics and magnetic sensing in noncollinear magnets.

cond-mat.mtrl-sci↗
arXiv2026

AI-accelerated metallized $\sigma$-bonding screening for superconductor discovery

The computational discovery of phonon-mediated superconductors is hindered by the prohibitive cost of density functional perturbation theory (DFPT). Here, guided by the metallized $\sigma$-bonding picture, we introduce the $\sigma$-bonding density of states ($\sigma$DOS) as an efficient physical descriptor to identify high-transition-temperature ($T_{\mathrm{c}}$) superconductors from density functional theory (DFT)-level electronic structure without explicit DFPT calculations. The evaluation of $\sigma$DOS can be further accelerated by a deep-learning DFT Hamiltonian method, enabling efficient large-scale screening for superconductors. Screening 2 million materials, we identify B$_{13}$Se as an ambient-pressure superconductor candidate with predicted $T_{\mathrm{c}} > 40$~K, together with a family of high-$T_{\mathrm{c}}$ B$_{13}X$ candidates, supporting the effectiveness of this discovery strategy. By bridging physics priors with AI acceleration, this study delivers an efficient and generalizable route for computational materials discovery in the AI era.

physics.comp-ph↗
arXiv2026

Saturated and Anisotropic Magnetostriction in an Altermagnet

Magnetostriction, a fundamental phenomenon bridging magnetism and mechanics, has enabled a broad spectrum of applications. For almost two centuries, it has been mainly investigated for ferromagnets. Regarding the magnetostriction of antiferromagnets (AFMs), limitedly known examples for both conventional collinear AFMs and noncollinear AFMs predominantly exhibit non-saturating magnetic-field dependence. Herein, we report an easily saturated magnetostriction effect in a prototypical altermagnet - MnTe, which is an emerging class of collinear AFMs with special crystal symmetries. For high-quality MnTe single crystals, the magnetostriction saturates under a moderate field of ~0.7 T with an intriguing two-fold-symmetry anisotropy. First-principles calculations reveal that the saturated and anisotropic magnetostriction originates from symmetry-allowed coupling between elastic strain and its N\'eel order parameter. These findings break the traditional wisdom on antiferromagnetic magnetostriction.

cond-mat.mtrl-sci↗
arXiv2026

Occupation Dynamics of Floquet-Volkov States and Spectral Sum Rule

Time-periodic light fields can dress electronic states in quantum materials, forming Floquet states whose dynamic occupation determines transient material properties. Here by using time- and angle-resolved photoemission spectroscopy (TrARPES), we reveal the transient occupation of Floquet-Volkov states in two semiconductors, black phosphorus and MoSe$_2$. While the occupation of the light-induced sidebands, directly reflected by TrARPES spectral weight, strongly depends on the driving field, we find that the total spectral weight obtained by summing up all sidebands is conserved upon below-gap driving. Our work provides critical insights into the Floquet population dynamics, which are essential for light-field tailoring of transient material properties.

cond-mat.mtrl-sci↗
arXiv2026

Exciton-roton mode in moir\'e fractional Chern insulators

Moir\'e fractional Chern insulators (FCIs) are a novel class of quantum matter that realizes fractional quantum Hall (FQH) physics in zero magnetic field and provides a platform for exploring unconventional collective excitations. Here we show that hybridization between the magneto-roton and moir\'e interband excitations gives rise to an exciton-roton mode absent in continuum FQH systems in the long-wavelength limit. Using exact diagonalization and a variational Bethe-Salpeter equation for twisted MoTe$_2$, we demonstrate that this hybridization is controlled by the quantum geometry and yields a mode that combines excitonic optical response with the characteristic FCI roton minimum. The resulting exciton-roton remains low-lying, with excitation energy below the interband transition, and acquires optical activity, leading to a double-peak spectroscopic signature. These results identify optical spectroscopy as a direct probe of collective excitations in moir\'e FCIs.

cond-mat.str-el↗
arXiv2026

Field-induced asymmetric band flattening and ideal quantum geometry in rhombohedral graphene

Rhombohedral graphene exhibits an exceptionally diverse array of correlated phases that depend sensitively on the displacement field. Compiling reported phases into a unified phase diagram reveals a pronounced field-dependent electron-hole asymmetry: correlated states on the hole-doped side emerge at small displacement fields, whereas the fractional quantum anomalous Hall effect (FQAHE) is observed exclusively on the electron-doped side under large displacement fields. This stark asymmetry highlights the need to understand how flat bands evolve with displacement fields. Here, we directly visualize the field-induced electron-hole asymmetric band flattening in rhombohedral pentalayer graphene (R5G) using nanospot angle-resolved photoemission spectroscopy with electrostatic gating. Beyond gap opening and spectral weight redistribution indicative of layer polarization, the gating field drives a strongly asymmetric modification of the flat bands: the flat valence band (FVB) evolves into an M-shaped dispersion at high field, whereas the flat conduction band (FCB) progressively flattens with increasing field. Comparison with calculations identifies critical parameters governing the band curvature of R5G, from which the resulting finite Berry curvature and near-ideal quantum geometry support the emergence of topological phases under electron doping at large fields. These results establish a direct link between the asymmetric phase diagram, band structure evolution, and quantum geometry, providing a microscopic framework for understanding correlated and topological phases in rhombohedral graphene.

cond-mat.mes-hall↗
arXiv2026

Spin-Valley-Mismatched Altermagnet for Giant Tunneling Magnetoresistance

Altermagnet-based heterojunctions have demonstrated magnetoresistive effects in experiments, however, a predictive theoretical model for non-ferromagnetic structures has remained elusive. In this work, we develop a tunneling-based spin-transport theory that explicitly incorporates the transverse-wavevector ($\bf{k}_\|$)-dependent spin polarization of an altermagnet's transport channels, enabling the prediction of giant tunneling magnetoresistance (TMR). Based on the theory, we predict that the altermagnet KV$_2$Se$_2$O can reach the extreme limit of magnetoresistance. By performing first-principles transport calculations, we verify that magnetic tunnel junctions using the metallic KV$_2$Se$_2$O as the electrodes and few-layer MgO as the spacer exhibit zero-bias magnetoresistance larger than $7.57\times10^7$\%, which is robust against the bias and thickness of the spacer. Our research provides a quantitative design principle for next-generation spin-electronic devices and establishes KV$_2$Se$_2$O/MgO/KV$_2$Se$_2$O as a leading candidate material system for room-temperature ultra-high-density non-volatile memory.

cond-mat.mtrl-sci↗
arXiv2026

Symmetry-guided and AI-accelerated design of intercalated transition metal dichalcogenides for antiferromagnetic spintronics

The advancement of antiferromagnetic spintronics depends on quantum materials with target symmetry-dictated functionalities, however, their systematic discovery is hindered by the immense configurational complexity of the available material space. Here, we introduce a symmetry-guided, AI-accelerated framework incorporating graph neural networks with high generalization ability to overcome this bottleneck. Based on fully intercalated transition metal dichalcogenides (iTMDs) and using only 200 relaxed partially intercalated structures for transfer learning, our model effectively explores more than 100,000 partially intercalated configurations and identifies 35 altermagnetic and 20 $T\tau$-antiferromagnetic ground-state candidates. Interestingly, we show that tuning spin-group symmetry through intercalant arrangement or magnetic ordering realizes a series of d-wave altermagnets in these hexagonal systems with high spin-charge conversion efficiency. Furthermore, we reveal plentiful $T\tau$-antiferromagnets enabling efficient N\'eel spin-orbit torque switching, driven by giant $T$-odd spin Edelstein susceptibilities. These results establish iTMDs as a versatile platform for spintronics and provide a general strategy for the accelerated design of symmetry-enforced quantum materials.

cond-mat.mtrl-sci↗
arXiv2026

Manipulating Charge Distribution in Moir\'e Superlattices by Light

In ordinary solids, nonlinear optical responses are typically studied in terms of unit-cell averages due to the angstr\"om-scale lattice constants. In contrast, moir\'e superlattices, characterized by a large length scale, unlock an often-overlooked degree of freedom: intra-supercell spatial variations of local observables. Here, we formulate the second-order direct current (DC) charge response in a spatially resolved manner, showing that even uniform optical illumination can drive a static, spatially non-uniform charge redistribution within a supercell. This effect is ubiquitous and cannot be forbidden by any crystalline symmetries. Furthermore, we identify a dominant contribution arising from diverging analytical response coefficients, which leads to linear-in-time growth of the redistribution in the absence of relaxation. This growth is driven by the convergence or divergence of local DC photocurrents. Applying our theory to twisted bilayer MoTe$_2$, we demonstrate strong, highly tunable charge modulation controlled by light intensity and frequency, opening a route to in situ, all-optical control of moir\'e-periodic electrostatic potentials. Our work underscores the importance of intra-cell degrees of freedom, which enable a qualitatively richer class of nonlinear optical responses in moir\'e superlattices.

cond-mat.mtrl-sci↗
arXiv2026

Machine Learning for Electron-phonon Interactions From Finite Difference

First-principles investigations of electron-phonon interactions (EPIs) play a crucial role in understanding a wide range of phenomena in physics and materials science. Among various approaches, the finite difference method offers a direct route to capture higher-order EPIs and is compatible with diverse electronic structure solvers. However, its considerable computational cost limits its broader application. To overcome this bottleneck, we present a machine learning electron-phonon interaction (MLEPI) pipeline that predicts force constants and electronic Hamiltonians for modeling EPIs from finite difference calculations, improving efficiency by orders of magnitude without compromising accuracy. The performance of MLEPI is validated by studying the temperature dependence of the electronic band properties in bilayer graphene, where both first- and second order EPIs are treated on an equal footing. Using a heterogeneous edge network, the pipeline integrates both interlayer and intralayer interactions, making it particularly suitable for studying multilayer materials. With its inherent adaptability and ease of transfer to other applications, our methodology provides a robust tool with a very favorable accuracy/efficiency balance for investigating EPIs in large-scale material systems.

cond-mat.mtrl-sci↗
arXiv2026

Multiple Layer-Selective Polar Charge Density Waves in ${\rm{EuTe}}_{4}$

${\rm{EuTe}}_{4}$ is a polar charge density wave (CDW) material, with giant thermal hysteresis and non-volatile state switching under electric and optical fields, attracting great attention in recent years. However, the in-depth understanding of these anomalous phenomena remains elusive. Herein, via first-principles calculations, we reveal that the polar CDW state in ${\rm{EuTe}}_{4}$ hosts a novel layer-selective nature, wherein multiple energetically close CDW configurations coexist and exhibit low interconversion energy barriers. Monte Carlo simulations indicate that the giant thermal hysteresis in ${\rm{EuTe}}_{4}$ originates from a phase transition mainly driven by the change of configurational entropy, around which the material hosts a metastable CDW state characterized by diverse local polar configurations breaking the out-of-plane translational symmetry. The configurational composition of this metastable CDW state can be effectively controlled by electric and optical fields, thereby enabling non-volatile state switching. Our theoretical findings align well with recent experimental observations in ${\rm{EuTe}}_{4}$ and pave the way for exploring the emerging phenomena and applications of polar CDW in multilayered systems.

cond-mat.mtrl-sci↗
arXiv2026

Two-dimensional Intrinsic Janus Structures: Design Principle and Anomalous Nonlinear Optics

Two-dimensional Janus structures have garnered rapidly growing attention across multidisciplinary fields. However, despite extensive theoretical and experimental efforts, a principle for designing intrinsic Janus materials remains elusive. Here, we propose a first-principles alloy theory based on cluster expansion, incorporating a strong repulsive interaction of a cation-mediated anion-pair cluster and refined short-range cluster-cluster competitions, to unravel the formation mechanism of intrinsic Janus structures with a distorted 1T phase among numerous competing phases. Our theory not only explains why intrinsic Janus structures are accidentally observed in RhSeCl and BiTeI which are composed of alloyed elements from different groups, but also accurately predicts a wide range of 1T-like intrinsic Janus materials that are ready for synthesis. Intriguingly, as demonstrated in the case of RhSeCl, we reveal that intrinsic Janus materials can exhibit anomalous second-harmonic generation (SHG) with a distinct quantum geometric effect, originating from strong lattice and chemical-potential mirror asymmetry. Furthermore, a novel skin effect unexpectedly emerges in finite-thickness RhSeCl, accompanied by a hidden SHG effect within the bulk region. Our theory paves the way for the ab initio design of intrinsic Janus materials, significantly accelerating progress in Janus science.

cond-mat.mtrl-sci↗
arXiv2026

DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations

In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.

cond-mat.mtrl-sci↗
arXiv2025

Moir\'e-modulated $\Gamma$ valley in twisted bilayer and twisted double-bilayer MoTe$_2$

Twisted MoTe$_2$ hosts intriguing correlated quantum phenomena including the fractional quantum anomalous Hall effect in twisted bilayer (t-BL) MoTe$_2$ near 3.7$^\circ$, which is sensitive to the twist angle and moir\'e superlattices. Here, we directly visualize the twist-angle-modulated electronic structure of t-BL and twisted double-bilayer (t-DBL) near this critical angle. We find that the moir\'e superlattice not only modifies the relative energy between $\Gamma$ and K valleys in t-BL MoTe$_2$, but also strongly reconstructs the $\Gamma$ valley for both t-BL and t-DBL. Specifically, the deep $p_z$-derived band at $\Gamma$ exhibits a distinct splitting that systematically varies with increasing twist angle. Theoretical analysis suggests that this modulation arises from the twist-angle-dependent lattice relaxation, especially interfacial corrugations. Our work directly visualizes the moir\'e-modulated electronic structure and provides key spectroscopic information of lattice relaxation and interlayer interactions underlying the physics of twisted MoTe$_2$.

cond-mat.str-el↗
arXiv2025

Two-Terminal Electrical Detection of the N\'eel Vector via Longitudinal Antiferromagnetic Nonreciprocal Transport

We propose a robust two-terminal electrical readout scheme for detecting the N\'eel vector orientation in antiferromagnetic (AFM) materials by leveraging longitudinal nonreciprocal transport driven by quantum metric dipoles. Unlike conventional readout mechanisms, our approach does not require spin-polarized electrodes, tunneling junctions, or multi-terminal geometries, offering a universal and scalable solution for AFM spintronics. As examples, we demonstrate pronounced second-order longitudinal nonlinear conductivity (LNC) in two-dimensional (2D) MnS and 3D CuMnAs, both of which exhibit clear sign reversal of LNC under 180$^\circ$ N\'eel vector reorientation. We show that this LNC is predominantly governed by the intrinsic, relaxation-time-independent quantum metric mechanism rather than the extrinsic nonlinear Drude effect. Our findings provide a practical and material-general pathway for electrically reading AFM memory states, with promising implications for next-generation AFM spintronic technologies.

cond-mat.mtrl-sci↗
arXiv2025

Topological surface states in {\gamma}-PtBi$_2$ evidenced by scanning tunneling microscopy

For the application of topological materials, the specific location of their topological surface states with respect to the Fermi level are important. {\gamma}-PtBi2 has been demonstrated to be a Weyl semimetal possessing superconducting Fermi arcs by photoemission spectroscopy. However, the evidence of its topological surface states is lacking by scanning tunneling microscopy (STM), which should be rather sensitive to detect the surface states. Here, we show multiple STM evidences for the existence of topological surface states in {\gamma}-PtBi2. We observe not only the step-edge and screw dislocation induced quasiparticle interference fringes, originating from the electron scatterings between the Fermi arcs of {\gamma}-PtBi2, but also the back-scattering prohibition related to the spin-flip process, which is the direct evidence for the topological nature of the surface states. Moreover, we demonstrate that the topological surface states are precisely located over a narrow energy range near the Fermi level, within which sharply enhanced intensity and slow spatial decay of quasiparticle interference are observed.

cond-mat.supr-con↗
arXiv2025

SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm, Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR), that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 times acceleration compared to conventional SR implementations that heavily rely on first principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

physics.comp-ph↗