arXiv subjects

Xudong Zhu

Publications and source records attributed to Xudong Zhu.

At least 19 recordsLinked to original sources

arXiv2026

ZStar: An automated toolkit for polarization, Born effective charges, dielectric response, and infrared and Raman spectra calculations

Born effective charges (BEC) link atomic displacements to polarization, connecting lattice vibrations to dielectric response and infrared activity. We present ZStar, an open-source Python toolkit that automates calculations of polarization, BEC, phonons, static and frequency-dependent dielectric response, and infrared and Raman spectra. ZStar supports calculations across all dimensionalities, including bulk crystals, two-dimensional materials, one-dimensional structures, and molecules, through an integrated workflow spanning input preparation, execution, and response analysis. Berry-phase polarization and real-space dipoles describe responses along periodic and open directions, respectively. Our unified, symmetry-adapted finite-displacement framework jointly determines BEC and zone-center force constants from the same calculations, improving computational efficiency while preserving numerical accuracy. Reusing the electronic Hamiltonian in the ABACUS+PYATB route enables Raman calculations without additional self-consistent calculations. Reproducible examples and a packaged agent skill support conventional and agent-assisted use. ZStar enables efficient, automated exploration of polarization, BEC, dielectric response, and vibrational spectra across all dimensionalities.

cond-mat.mtrl-sci↗

arXiv2026

Interface-Controlled Phase Stability in Polymorphic HfO$_2$ Revealed by Machine-Learning Atomistic Simulations

HfO$_2$ exhibits rich polymorphism, and competition among different phases underpins many of its functional properties. Yet bulk free-energy relations alone cannot explain phase selection at mixed-phase boundaries, where interface orientation and structural continuity constrain collective rearrangements. Here, using machine-learning atomistic simulations and a Hf-centered local phase classification scheme, we show that crystallographic interface matching redirects phase competition and accessible transformation pathways. The M(100)/T(100) interface remains pinned as an M/T mixture throughout 3 ns simulations from 300 to 1800 K. M/PO, M/AO, and PO/AO interfaces retain two-phase coexistence up to 900 K, whereas all T/PO interfaces become PO-dominant. At 1800 K, all non-M interfaces become T-dominant, while M-containing interfaces retain a monoclinic majority. Nudged elastic band calculations reveal lower-barrier routes through interface states. For M(100)$\rightarrow$T(100), the interface-mediated route in a long cell ($\sim$12 nm) yields a barrier of 136.03 meV/f.u., 29.2 % lower than the direct route in a short cell ($\sim$3 nm). This difference is associated with sequential phase-front motion absent from the short cell. These results identify phase boundaries as active participants in phase stability and transformation and establish interface orientation and crystallographic matching as variables for stabilizing metastable polymorphs and directing phase conversion in HfO$_2$.

cond-mat.mtrl-sci↗

arXiv2026

Cartesian tensor equivariant machine-learning force field for spin-dependent atomistic simulations

Magnetic materials exhibit an intricate coupling between atomic structure and spin degrees of freedom, posing a fundamental challenge for atomistic simulations across experimentally relevant length and time scales. Here we introduce HotPP-Spin, a spin-dependent extension of HotPP for magnetic machine learning interatomic potentials, built on Cartesian tensor equivariant message passing. Atomic magnetic moments are treated as explicit axial-vector degrees of freedom, while spatial-inversion and time-reversal parities are propagated through the tensor couplings. This construction provides a unified representation of exchange-dominated and spin-orbit-induced interactions without imposing predefined analytical interaction forms. A scalar spin-dependent potential energy surface yields energy-conserving atomic forces and magnetic effective fields through differentiation. Benchmarks spanning collinear magnetism, noncollinear magnetism, and spin-orbit-coupling-induced magnetic anisotropy show that HotPP-Spin accurately describes magnetic energy landscapes, magnetic forces, and magnetic-order-dependent energy-volume relations within the same general framework. For H-phase monolayer VSe$_2$, stochastic spin-dynamics simulations using the learned magnetic effective fields locate the finite-size magnetic ordering crossover at 415--435~K, in close numerical agreement with the reported experimental value of $418.5\pm7.8$~K. These results establish Cartesian tensor message passing as a general route for connecting first-principles magnetic energetics with large-scale atomistic simulations of coupled structural and spin phenomena.

physics.comp-ph↗

arXiv2026

Quantum Anomalous Hall Effect in $d^{10}$ Oxide Monolayers

Quantum anomalous Hall effect (QAHE) arises from the interplay between magnetic order and spin-orbit coupling, which opens up a topologically nontrivial band gap to host chiral edge states in the absence of magnetic field. So far, magnetic order of QAHE usually originates from partially filled transition-metal $d$ orbitals or correlation-driven moiré bands. Here, we propose an experimentally accessible family of two-dimensional oxides, M$_2$DO$_6$ (M = Zn, Cd; D = Se, Te), that can realize QAHE from the half-filled O-$2p$ orbital induced spontaneous ferromagnetism. In M$_2$DO$_6$ monolayers, spin-polarized Dirac points appear at K/K$^{\prime}$ valleys and along $Γ$-K/$Γ$-K$^{\prime}$ lines. $C_3$ rotational symmetry then generates eight symmetry-related crossings in the first Brillouin zone. Upon gap opening by spin-orbit coupling, each massive Dirac point contributes half Chern number, resulting in a high-Chern-number QAHE phase with $\mathcal{C}=4$. We establish cation deintercalation as a general strategy to activate O-$2p$ ferromagnetism in oxides. Our finding provides a route to realize QAHE from O-$2p$ ferromagnetism and offers design principles applicable to oxygen-based magnetic topology platforms beyond conventional $d$-electron systems.

cond-mat.mes-hall↗

arXiv2026

NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction

Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. Crystal symmetry plays a crucial role in CSP, but given the composition in a unit cell, existing methods either struggle with the NP-hard combinatorial challenge of enforcing symmetry rigorously or rely on retrieving known templates, inherently limiting both physical fidelity and the discovery of genuinely new materials. To address this challenge, we introduce NextCrystal, a symmetry-driven generative framework that employs large language models to encode chemical semantics and directly generate fine-grained Wyckoff site patterns from atomic stoichiometry, eliminating reliance on database lookups. To overcome the combinatorial complexity of site assignments, we incorporate domain knowledge via an efficient, linear-complexity heuristic beam search, rigorously enforcing algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, the framework constrains the stochastic generative trajectory to a physically plausible geometric manifold. NextCrystal achieves state-of-the-art performance on stability, uniqueness, and novelty (SUN) benchmarks, as well as superior structural matching, establishing a rigorous paradigm for exploring previously unexplored crystallographic space without relying on prior structural templates. As a representative application, first-principles screening of HfO2 candidates generated by NextCrystal identifies a previously unreported dynamically stable Pnma phase, 0.056~eV/atom lower in energy than the conventional high-pressure Pnma phase.

cond-mat.mtrl-sci↗

arXiv2026

Nonvolatile single-ion memory with picosecond switching

The rapid development of artificial intelligence (AI), Internet of Things (IoT), and edge computing applications has posed severe challenges to conventional memory technologies in terms of density, speed, and energy consumption. Herein, a single-ion transport mechanism is proposed to achieve picosecond (ps) switching capability. For monolayer hexagonal boron nitride (h-BN) with single-atom vacancy defects, first-principles calculations reveal that single-ion penetration across the BN plane dominates the resistive switching. The trapping and release of a single ion correspond to different states of the memory device for one bit of information. Experimentally fabricated single-ion memory exhibits nonvolatile resistive switching with ultra-fast switching speed of 20 ps and ultra-low energy consumption of 310 aJ/bit. This high performance is attributed to the extremely short distance for the single ion to travel through. Such devices pave the way for the realization of high-performance nonvolatile memory with ultra-fast speed, ultra-low energy consumption, and high storage density, that is called the "Unified Memory" long desired by the whole industry.

physics.app-ph↗

arXiv2026

CPTCs Drive Somatic-Visceral Communication via the Wnt Axis in Somatic Mechanotherapy: A Single-Cell Deep Learning Study

Somatic mechanical stimulation (e.g., acupuncture) exerts systemic immunomodulatory effects, yet the cellular bridge translating peripheral physical force into visceral repair remains elusive. Here, employing a custom interpretable deep learning framework (CARSS) on single-cell RNA sequencing data, we identify CD34$^{+}$PDGFR$α$$^{+}$ telocytes (CPTCs) as the primary mechanosensors in both fascia and colon during bacterial colitis. We show that somatic mechanotherapy triggers an AP-1/Hsp70-dependent transcriptional program in fascial CPTCs, inducing systemic Wnt elevation, which elicits a "transcriptional resonance" in colonic CPTCs, reprogramming their communication network from an inflammatory amplifier to a Wnt-driven regenerative hub. Mechanistically, this axis activates epithelial $β$-catenin/Myc signaling, suppressing apoptosis and restoring barrier integrity independent of immune cells. Our findings define a CPTC-Driven Mechano-Resonance Axis, where CPTCs serve as synchronized relay stations that convert local mechanical cues into systemic regenerative microenvironments.

q-bio.BM↗

arXiv2025

AbsTopK: Rethinking Sparse Autoencoders For Bidirectional Features

Sparse autoencoders (SAEs) have emerged as powerful techniques for interpretability of large language models (LLMs), aiming to decompose hidden states into meaningful semantic features. While several SAE variants have been proposed, there remains no principled framework to derive SAEs from the original dictionary learning formulation. In this work, we introduce such a framework by unrolling the proximal gradient method for sparse coding. We show that a single-step update naturally recovers common SAE variants, including ReLU, JumpReLU, and TopK. Through this lens, we reveal a fundamental limitation of existing SAEs: their sparsity-inducing regularizers enforce non-negativity, preventing a single feature from representing bidirectional concepts (e.g., male vs. female). This structural constraint fragments semantic axes into separate, redundant features, limiting representational completeness. To address this issue, we propose AbsTopK SAE, a new variant derived from the $\ell_0$ sparsity constraint that applies hard thresholding over the largest-magnitude activations. By preserving both positive and negative activations, AbsTopK uncovers richer, bidirectional conceptual representations. Comprehensive experiments across four LLMs and seven probing and steering tasks show that AbsTopK improves reconstruction fidelity, enhances interpretability, and enables single features to encode contrasting concepts. Remarkably, AbsTopK matches or even surpasses the Difference-in-Mean method, a supervised approach that requires labeled data for each concept and has been shown in prior work to outperform SAEs.

cs.LG↗

arXiv2025

Topological Origin of Intrinsic High Chern Numbers in Two-Dimensional M$_2$X$_2$ Materials

Despite sharing a common lattice structure, monolayer M$_2$X$_2$ compounds realize quantum anomalous Hall phases with distinct Chern numbers, a striking phenomenon that has not been fully exploared. Combining first-principles calculations with symmetry analysis and tight-binding models, we identify two generic band-inversion mechanisms governed by the orbital composition and symmetry representations of 3$d$ states near the Fermi level. When $d_{xz}/d_{yz}$ orbtials dominate, a doubly degenerate $Γ$-point inversion yields $C=1$; otherwise, inversions occur along $Γ$-X and $Γ$-Y at four $C_4$-related momenta, whose Berry-curvature contributions add to give $C=2$, distinct from scenarios relying on multiple bands inversions at a single $\mathbf{k}$ point. The same mechanism consistently explains related two-dimensional systems, including LiFeSe, KTiSb, MgFeP, and Janus M$_2$X$_2$ derivatives. The mechanism provide practical guidance for screening and engineering tunable high-Chern-number insulators.

cond-mat.mtrl-sci↗

arXiv2025

From Emergence to Control: Probing and Modulating Self-Reflection in Language Models

Self-reflection -- the ability of a large language model (LLM) to revisit, evaluate, and revise its own reasoning -- has recently emerged as a powerful behavior enabled by reinforcement learning with verifiable rewards (RLVR). While self-reflection correlates with improved reasoning accuracy, its origin and underlying mechanisms remain poorly understood. In this work, {\it we first show that self-reflection is not exclusive to RLVR fine-tuned models: it already emerges, albeit rarely, in pretrained models}. To probe this latent ability, we introduce Reflection-Inducing Probing, a method that injects reflection-triggering reasoning traces from fine-tuned models into pretrained models. This intervention raises self-reflection frequency of Qwen2.5 from 0.6\% to 18.6\%, revealing a hidden capacity for reflection. Moreover, our analysis of internal representations shows that both pretrained and fine-tuned models maintain hidden states that distinctly separate self-reflective from non-reflective contexts. Leveraging this observation, {\it we then construct a self-reflection vector, a direction in activation space associated with self-reflective reasoning}. By manipulating this vector, we enable bidirectional control over the self-reflective behavior for both pretrained and fine-tuned models. Experiments across multiple reasoning benchmarks show that enhancing these vectors improves reasoning performance by up to 12\%, while suppressing them reduces computational cost, providing a flexible mechanism to navigate the trade-off between reasoning quality and efficiency without requiring additional training. Our findings further our understanding of self-reflection and support a growing body of work showing that understanding model internals can enable precise behavioral control.

cs.LG↗

arXiv2025

Ultra-rapid broadband mid-infrared spectral tuning and sensing

Tunable mid-infrared lasers are essential for optical sensing and imaging. Existing technologies, however, face challenges in simultaneously achieving broadband spectral tunability and ultra-rapid scan rates, limiting their utility in dynamic scenarios such as real-time characterization of multiple molecular absorption bands. Here, we present a high-speed approach for broadband wavelength sweeping in the mid-infrared region, leveraging spectral focusing via difference frequency generation between a chirped fiber laser and an asynchronous, frequency-modulated electro-optic comb. This method enables pulse-to-pulse spectral tuning at a speed of 5.6 THz/us with 380 elements. Applied to spectroscopic sensing, our technique achieves broad spectral coverage (2600-3780 cm-1) with moderate spectral resolution (8 cm-1) and rapid acquisition times (6.3 us). Notably, the controllable electro-optic comb facilitates high scan rates of up to 2 Mscans/s across the full spectral range (corresponding to a speed of 60 THz/us), with trade-offs in number of elements (~30) and spectral point spacing or resolution (33 cm-1). Nevertheless, these capabilities make our platform highly promising for applications such as flow cytometry, chemical reaction monitoring, and mid-IR ranging and imaging.

physics.optics↗

arXiv2024

Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid Deep Learning Framework for Electronic-Structure Hamiltonian Prediction

Deep learning for predicting the electronic-structure Hamiltonian of quantum systems necessitates satisfying the covariance laws, among which achieving SO(3)-equivariance without sacrificing the non-linear expressive capability of networks remains unsolved. To navigate the harmonization between equivariance and expressiveness, we propose a deep learning method synergizing two distinct categories of neural mechanisms as a two-stage encoding and regression framework. The first stage corresponds to group theory-based neural mechanisms with inherent SO(3)-equivariant properties prior to the parameter learning process, while the second stage is characterized by a non-linear 3D graph Transformer network we propose, featuring high capability on non-linear expressiveness. The novel combination lies in the point that, the first stage predicts baseline Hamiltonians with abundant SO(3)-equivariant features extracted, assisting the second stage in empirical learning of equivariance; and in turn, the second stage refines the first stage's output as a fine-grained prediction of Hamiltonians using powerful non-linear neural mappings, compensating for the intrinsic weakness on non-linear expressiveness capability of mechanisms in the first stage. Our method enables precise, generalizable predictions while capturing SO(3)-equivariance under rotational transformations, and achieves state-of-the-art performance in Hamiltonian prediction on six benchmark databases.

physics.comp-ph↗

arXiv2024

FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction

Document-level Relation Extraction (DocRE) aims to identify relation labels between entities within a single document. It requires handling several sentences and reasoning over them. State-of-the-art DocRE methods use a graph structure to connect entities across the document to capture dependency syntax information. However, this is insufficient to fully exploit the rich syntax information in the document. In this work, we propose to fuse constituency and dependency syntax into DocRE. It uses constituency syntax to aggregate the whole sentence information and select the instructive sentences for the pairs of targets. It exploits the dependency syntax in a graph structure with constituency syntax enhancement and chooses the path between entity pairs based on the dependency graph. The experimental results on datasets from various domains demonstrate the effectiveness of the proposed method. The code is publicly available at this url.

cs.CL↗

arXiv2022

Valley-polarized quantum anomalous Hall effect in van der Waals heterostructures based on monolayer jacutingaite family materials

We numerically study the general valley polarization and anomalous Hall effect in van der Waals (vdW) heterostructures based on monolayer jacutingaite family materials Pt$_{2}$AX$_{3}$ (A = Hg, Cd, Zn; X = S, Se, Te). We perform a systematic study on the atomic, electronic, and topological properties of vdW heterostructures composed of monolayer Pt$_{2}$AX$_{3}$ and two-dimensional ferromagnetic insulators. We show that four kinds of vdW heterostructures exhibit valley-polarized quantum anomalous Hall phase, i.e., Pt$_{2}$HgS$_{3}$/NiBr$_{2}$, Pt$_{2}$HgSe$_{3}$/CoBr$_{2}$, Pt$_{2}$HgSe$_{3}$/NiBr$_{2}$, and Pt$_{2}$ZnS$_{3}$/CoBr$_{2}$, with a maximum valley splitting of 134.2 meV in Pt$_{2}$HgSe$_{3}$/NiBr$_{2}$ and sizable global band gap of 58.8 meV in Pt$_{2}$HgS$_{3}$/NiBr$_{2}$. Our findings demonstrate an ideal platform to implement applications on topological valleytronics.

cond-mat.mes-hall↗

arXiv2022

Pressure-induced dimensional crossover in a kagome superconductor

The recently discovered kagome superconductors AV3Sb5 exhibit tantalizing high-pressure phase diagrams, in which a new dome-like superconducting phase emerges under moderate pressure. However, its origin is as yet unknown. Here, we carried out the high-pressure electrical measurements up to 150 GPa, together with the high-pressure X-ray diffraction measurements and first-principles calculations on CsV3Sb5. We find the new superconducting phase to be rather robust and inherently linked to the interlayer Sb2-Sb2 interactions. The formation of Sb2-Sb2 bonds at high pressure tunes the system from two-dimensional to three-dimensional and pushes the Pz orbital of Sb2 upward across the Fermi level, resulting in enhanced density of states and increase of TC. Our work demonstrates that the dimensional crossover at high pressure can induce a topological phase transition and is related to the abnormal high-pressure TC evolution. Our findings should apply for other layered materials.

cond-mat.supr-con↗

arXiv2016

Effects of nickel doping on the preferred orientation and oxidation potential of Ti/Sb-SnO2 anodes prepared by spray pyrolysis

Nickel and antimony co-doped Ti/SnO2 (Ti/Ni-Sb-SnO2) anodes were prepared by spray pyrolysis. Effects of nickel concentration on the structure and onset potential for oxygen evolution of Ti/Ni-Sb-SnO2 anodes have been systematically investigated. XRD analyses suggest that SnO2 thin films grow in preferential orientation along (101) plane as the nickel concentration increases. The enhanced onset potential of oxygen is above 2.4 V vs NHE due to the introduction of nickel doping, and increases slightly with the nickel concentration. The calculated results show that work function of Ni/Sb co-doped SnO2 also increases with the Ni doping level, which contributes to the enhancement of onset potential for oxygen evolution.

cond-mat.mtrl-sci↗

arXiv2015

Structured Matching Pursuit for Reconstruction of Dynamic Sparse Channels

In this paper, by exploiting the special features of temporal correlations of dynamic sparse channels that path delays change slowly over time but path gains evolve faster, we propose the structured matching pursuit (SMP) algorithm to realize the reconstruction of dynamic sparse channels. Specifically, the SMP algorithm divides the path delays of dynamic sparse channels into two different parts to be considered separately, i.e., the common channel taps and the dynamic channel taps. Based on this separation, the proposed SMP algorithm simultaneously detects the common channel taps of dynamic sparse channels in all time slots at first, and then tracks the dynamic channel taps in each single time slot individually. Theoretical analysis of the proposed SMP algorithm provides a guarantee that the common channel taps can be successfully detected with a high probability, and the reconstruction distortion of dynamic sparse channels is linearly upper bounded by the noise power. Simulation results demonstrate that the proposed SMP algorithm has excellent reconstruction performance with competitive computational complexity compared with conventional reconstruction algorithms.

cs.IT↗

arXiv2015

Tracking A Dynamic Sparse Channel Via Differential Orthogonal Matching Pursuit

This paper considers the problem of tracking a dynamic sparse channel in a broadband wireless communication system. A probabilistic signal model is firstly proposed to describe the special features of temporal correlations of dynamic sparse channels: path delays change slowly over time, while path gains evolve faster. Based on such temporal correlations, we then propose the differential orthogonal matching pursuit (D-OMP) algorithm to track a dynamic sparse channel in a sequential way by updating the small channel variation over time. Compared with other channel tracking algorithms, simulation results demonstrate that the proposed D-OMP algorithm can track dynamic sparse channels faster with improved accuracy.

cs.IT↗