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Yu Xie

Publications and source records attributed to Yu Xie.

At least 37 records · Page 2Linked to original sources
arXiv2026

Domain-Wall Mediated Polarization Switching in Ferroelectric AlScN: Strain Relief and Field-Dependent Dynamics

While scandium-doped aluminum nitride (AlScN) exhibits robust ferroelectricity and excellent thermal stability, its utility is limited by an exceptionally high coercive field ($E_c$) for polarization switching. Unraveling the atomistic switching dynamics is therefore critical for tailoring $E_c$. Here, we combine density functional theory and machine-learning molecular dynamics to elucidate the polarization switching mechanisms in AlScN over various Sc concentrations and applied electric fields. We find that excessive lattice strain strictly prohibits collective polarization switching, but the pre-existing domain walls relieve strain and lead to a distinct switching dynamics -- dictating a field-dependent switching mechanism. At low electric fields, switching occurs via gradual domain-wall propagation consistent with the Kolmogorov-Avrami-Ishibashi model. In contrast, high fields stimulate additional nucleation, driving a rapid, homogeneous reversal process described by the simultaneous non-linear nucleation and growth model. These findings highlight the critical role of domain-wall dynamics and suggest domain engineering as a viable strategy to tailor coercive fields in AlScN and related ferroelectrics.

cond-mat.mtrl-sci↗
arXiv2026

Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity

High-fidelity experimental characterization of turbulent premixed flames remains limited by the cost and complexity of advanced diagnostics, particularly under elevated pressures and intense turbulence where measurements of coupled flame morphology and burning dynamics are sparse. Here, we develop a hierarchical multi-fidelity neural network framework (MuFiNNs) to address this challenge by integrating sparse high-fidelity experimental data with structured low-fidelity representations encoding dominant physical trends. The framework combines hierarchical low-fidelity construction with nonlinear multi-fidelity correction to learn coupled geometric and reactive flame behavior while recovering discrepancies that simplified models alone cannot capture. The methodology is applied to expanding turbulent premixed flames to predict three-dimensional flame wrinkling dynamics and turbulent mass burning velocity across varying fuels, pressures, and turbulence intensities. Using experimentally informed low-fidelity trend models with sparse high-fidelity measurements, MuFiNNs accurately reconstruct observed flame behavior, enable interpolation across unseen operating conditions, and demonstrate robust extrapolation beyond the training domain. Importantly, the framework remains effective in noisy, weakly structured, or experimentally inaccessible regimes where conventional data-driven approaches often fail. These results show that hierarchical multi-fidelity learning provides a scalable and physically grounded strategy for predictive combustion modeling in data-limited regimes. More broadly, this work establishes multi-fidelity scientific machine learning as a practical framework for extracting physically meaningful predictive models from sparse experiments, particularly for instability-dominated and turbulence-sensitive reactive flows where high-fidelity data acquisition is demanding.

cs.LG↗
arXiv2026

Effective phonon models based on symmetry-adapted multipole basis -- Hidden chiral phonon angular momentum splitting in ferroaxial systems

We propose a symmetry-based framework for constructing effective harmonic phonon models using a symmetry-adapted multipole basis. By decomposing the force-constant matrix into bond-centered electric multipoles, we identify the minimal microscopic ingredients responsible for phonon angular-momentum splitting. Applying this framework to a minimal zigzag-chain model, we show that ferroaxial order gives rise to a hidden sublattice-resolved chiral phonons, while an additional polar contribution leads to finite global chirality. Our results provide a unified symmetry-based description of hidden and emergent phonon phenomena and suggest a route to control phonon properties via electronic orderings and external fields.

cond-mat.mtrl-sci↗
arXiv2026

SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress

The widespread deployment of text-to-image diffusion models is significantly challenged by the generation of visually harmful content, such as sexually explicit content, violence, and horror imagery. Common safety interventions, ranging from input filtering to model concept erasure, often suffer from two critical limitations: (1) a severe trade-off between safety and context preservation, where removing unsafe concepts degrades the fidelity of the safe content, and (2) vulnerability to adversarial attacks, where safety mechanisms are easily bypassed. To address these challenges, we propose SafeCtrl, a Region-Aware safety control framework operating on a Detect-Then-Suppress paradigm. Unlike global safety interventions, SafeCtrl first employs an attention-guided Detect module to precisely localize specific risk regions. Subsequently, a localized Suppress module, optimized via image-level Direct Preference Optimization (DPO), neutralizes harmful semantics only within the detected areas, effectively transforming unsafe objects into safe alternatives while leaving the surrounding context intact. Extensive experiments across multiple risk categories demonstrate that SafeCtrl achieves a superior trade-off between safety and fidelity compared to state-of-the-art methods. Crucially, our approach exhibits improved resilience against adversarial prompt attacks, offering a precise and robust solution for responsible generation.

cs.CV↗
arXiv2026

From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration

Recommendation feeds work well when people are simply browsing, and search works well when they can formulate a query. Between these two cases is a common but poorly supported state: users feel that their feed has become repetitive, yet cannot clearly specify what they want instead. We refer to this state as vague intent. We present Red-Rec, an AI-supported exploration interface for this middle ground. After a period of browsing, the system summarizes patterns in the current feed (e.g., dominant content categories and possible latent interests), offers clickable exploration options, asks at most one follow-up question, and then gradually blends new content into the feed. The design is motivated by a formative study which found that users often recognize feed staleness but struggle to articulate alternatives, suggesting the need for proactive and low-effort interaction.We evaluated Red-Rec in a mixed-design lab study against three comparison conditions: a passive feed, search, and a user-initiated chat interface. Compared with user-initiated chat, Red-Rec led to broader exploration, higher serendipity ratings, and lower interaction effort. Participants in the AI-initiated condition typed very little , relying mainly on option selection, whereas participants in the user-initiated chat condition typed substantially more . We discuss how proactive, option-based AI support can help users move beyond repetitive feeds without undermining their sense of control, and we outline design implications for recommendation interfaces that support open-ended exploration.

cs.HC↗
arXiv2026

IdentityGuard: Context-Aware Restriction and Provenance for Personalized Synthesis

The nature of personalized text-to-image models poses a unique safety challenge that generic context-blind methods are ill-equipped to handle. Such global filters create a dilemma: to prevent misuse, they are forced to damage the model's broader utility by erasing concepts entirely, causing unacceptable collateral damage.Our work presents a more precisely targeted approach, built on the principle that security should be as context-aware as the threat itself, intrinsically bound to the personalized concept. We present IDENTITYGUARD, which realizes this principle through a conditional restriction that blocks harmful content only when combined with the personalized identity, and a concept-specific watermark for precise traceability. Experiments show our approach prevents misuse while preserving the model's utility and enabling robust traceability. By moving beyond blunt, global filters, our work demonstrates a more effective and responsible path toward AI safety.

cs.CR↗
arXiv2026

TextFlux: An OCR-Free DiT Model for High-Fidelity Multilingual Scene Text Synthesis

Diffusion-based scene text synthesis has progressed rapidly, yet existing methods commonly rely on additional visual conditioning modules and require large-scale annotated data to support multilingual generation. In this work, we revisit the necessity of complex auxiliary modules and further explore an approach that simultaneously ensures glyph accuracy and achieves high-fidelity scene integration, by leveraging diffusion models' inherent capabilities for contextual reasoning. To this end, we introduce TextFlux, a DiT-based framework that enables multilingual scene text synthesis. The advantages of TextFlux can be summarized as follows: (1) OCR-free model architecture. TextFlux eliminates the need for OCR encoders (additional visual conditioning modules) that are specifically used to extract visual text-related features. (2) Strong multilingual scalability. TextFlux is effective in low-resource multilingual settings, and achieves strong performance in newly added languages with fewer than 1,000 samples. (3) Streamlined training setup. TextFlux is trained with only 1% of the training data required by competing methods. (4) Controllable multi-line text generation. TextFlux offers flexible multi-line synthesis with precise line-level control, outperforming methods restricted to single-line or rigid layouts. Extensive experiments and visualizations demonstrate that TextFlux outperforms previous methods in both qualitative and quantitative evaluations.

cs.CV↗
arXiv2026

Revisiting Phase Stability and Superconductivity in Ca-H Superhydrides with Anharmonic Effects

The prediction of superconductivity above 200 K in CaH$_6$ revolutionized research on hydrogen-rich superconductors, and subsequent experiments have verified this prediction, while unidentified peaks in XRD and the decrease in superconducting temperature upon decompression indicate that unresolved issues remain. In this work, we reconstructed the accurate temperature-pressure phase diagram of the Ca-H system and determined the stability ranges of its candidate superconducting phases by considering anharmonic effects. Our results demonstrate that type-I clathrate Ca$_8$H$_{46-delta}$ structures become thermodynamically stable at 0 K when anharmonic effects are considered. Notably, we found that the previously predicted CaH$_6$ phase achieves stability above 500 K, underscoring the significant role of temperature and anharmonic effects in stabilizing this intriguing high-pressure phase. Our findings offer insights into the structure and superconducting mechanisms of hydrides.

cond-mat.supr-con↗
arXiv2026

SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation

Reinforcement learning-based preference optimization is increasingly used to align list-wise generative recommenders with complex, multi-objective user feedback, yet existing optimizers such as Gradient-Bounded Policy Optimization (GBPO) exhibit structural limitations in recommendation settings. We identify a Symmetric Conservatism failure mode in which symmetric update bounds suppress learning from rare positive signals (e.g., cold-start items), static negative-sample constraints fail to prevent diversity collapse under rejection-dominated feedback, and group-normalized multi-objective rewards lead to low-resolution training signals. To address these issues, we propose SAGE (Sequence-level Adaptive Gradient Evolution), a unified optimizer designed for list-wise generative recommendation. SAGE introduces sequence-level signal alignment via a geometric-mean importance ratio and a decoupled multi-objective advantage estimator to reduce token-level variance and mitigate reward collapse, together with asymmetric adaptive bounding that applies positive Boost updates to successful slates and an entropy-aware penalty to discourage low-diversity failures. Experiments on Amazon Product Reviews and the large-scale RecIF-Bench demonstrate consistent improvements in top-K accuracy, cold-start recall, and diversity across both Semantic-ID and native-text action spaces, while preserving numerical stability during training. These results suggest that asymmetric, sequence-aware policy optimization provides a principled and effective framework for addressing optimization failures in generative recommendation.

cs.LG↗
arXiv2026

A General Theory of Chiral Splitting of Magnons in Two-Dimensional Magnets

Magnons in antiferromagnets exhibit two chiral modes, providing an intrinsic degree of freedom for magnon-based computing architectures and spintronic devices. Electrical control of chiral splitting is crucial for applications, but remains challenging. Here, we propose the concept of extrinsic chiral splitting, involving alternating and ferrimagnet-like types, which can be induced and controlled by an electric field. A symmetry framework based on 464 collinear spin layer groups is established to classify chiral splitting characteristics and electric field responses in two-dimensional magnets. We further elucidate how the spin layer group determines the type of alternating chiral splitting and the dominant lowest-order magnetic exchange interaction. We demonstrate electric-field control over the magnitude and sign of the chiral splitting, enabling control of the spin Seebeck and Nernst effects related to thermal spin transport. This work provides a general theory for electric field manipulation of magnon chirality, paving the way for low-power magnonic logic devices.

cond-mat.mtrl-sci↗
arXiv2026

The Impact of Ionic Anharmonicity on Superconductivity in Metal-Stuffed B-C Clathrates

Metal-stuffed B$-$C compounds with sodalite clathrate structure have captured increasing attention due to their predicted exceptional superconductivity above liquid nitrogen temperature at ambient pressure. However, by neglecting the quantum lattice anharmonicity, the existing studies may result in an incomplete understanding of such a lightweight system. Here, using state-of-the-art ab initio methods incorporating quantum effects and machine learning potentials, we revisit the properties of a series of $XY$$\text{B}_{6}\text{C}_{6}$ clathrates where $X$ and $Y$ are metals. Our findings show that ionic quantum and anharmonic effects can harden the $E_g$ and $E_u$ vibrational modes, enabling the dynamical stability of 15 materials previously considered unstable in the harmonic approximation, including materials with previously unreported ($XY$)$^{1+}$ state, which is demonstrated here to be crucial to reach high critical temperatures. Further calculations based on the anisotropic Migdal-Eliashberg equation demonstrate that the $T_\text{c}$ values for KRb$\text{B}_{6}\text{C}_{6}$ and Rb$\text{B}_{3}\text{C}_{3}$ among these stabilized compounds are 102 and 115 K at 0 and 15 GPa, respectively, both being higher than $T_\text{c}$ of 92 K of KPb$\text{B}_{6}\text{C}_{6}$ at the anharmonic level. These record-high $T_\text{c}$ values, surpassing liquid nitrogen temperatures, emphasize the importance of anharmonic effects in stabilizing B-C clathrates with large electron-phonon coupling strength and advancing the search for high-$T_\text{c}$ superconductivity at (near) ambient pressure.

cond-mat.supr-con↗
arXiv2025

Spinless electric toroidal multipoles in ferroaxial ${\rm K_2Zr(PO_4)_2}$ revealed by symmetry-adapted closest Wannier analysis

From a symmetry perspective, ferroaxial order belongs to the same symmetry as time-reversal-even pseudovectors. Experimentally, ${\rm K_2Zr(PO_4)_2}$ is known to undergo a displacive-type phase transition from a non-ferroaxial to a ferroaxial phase. To identify the key microscopic ingredients driving this transition, we carry out a quantitative analysis combining density-functional theory calculations and symmetry-adapted closest Wannier analysis. As a result, we show that electric toroidal dipole, electric toroidal octupole, and electric hexadecapole, which belong to the same irreducible representation, make dominant contributions to the ferroaxial transition. In particular, we find that spinless electric toroidal octupoles, which originate from spin-independent off-diagonal real hopping between the $p$ orbitals on P and O atoms and between the $d$ orbitals on Zr atoms and $p$ orbitals on O atoms, provide the most significant contributions. Moreover, we explicitly analyze the orbital characters involved in the relevant hybridizations associated with these multipoles. We further show that the relativistic spin--orbit coupling has a negligible influence on the ferroaxial transition. These results demonstrate that spin-independent orbital hybridization between different orbitals on different atoms plays a crucial role in inducing the ferroaxial transition.

cond-mat.mtrl-sci↗
arXiv2025

Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics

Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machine learning molecular dynamics (MLMD) simulations to gain insights into SiC decomposition and phase transitions. Our approach relies on a Bayesian active learning workflow to efficiently train an accurate machine learning force field on density functional theory data. Our large-scale simulations provide direct indication that melting of SiC proceeds incongruently via decomposition into silicon-rich and carbon phases at high temperature and pressure. During cooling at high pressures, carbon nanoclusters nucleate and grow within the homogeneous molten liquid. During heating, the decomposed mixture reversibly transitions back into a homogeneous SiC liquid. The full pressure-temperature phase diagram of SiC is systematically constructed using MLMD simulations, providing new understanding of the nature of phases, resolving long-standing inconsistencies from previous experiments and yielding technologically relevant implications for processing and deposition of this material.

cond-mat.mtrl-sci↗
arXiv2025

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design

Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled neural networks potential to address these limitations. The generalizability of the potential is achieved by sampling regions across the local minima of the potential energy surface, where the self-evolving pipeline autonomously refines the potential iteratively while minimizing human intervention. The workflow is validated on Mg-Ca-H ternary and Be-P-N-O quaternary systems by exploring nearly 10 million configurations, demonstrating substantial speedup compared to first-principles calculations. These results underscore the effectiveness of our approach in accelerating the exploration and discovery of complex multi-component functional materials.

cond-mat.mtrl-sci↗
arXiv2025

UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials

Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from critical limitations: training objectives seldom target function directly, discrete sequences and continuous coordinates are optimized in isolation, and conformational ensembles are under-modeled. We present UniGenX, a unified generative foundation model that addresses these gaps by co-generating sequences and coordinates under direct functional and property objectives across proteins, molecules, and materials. UniGenX represents heterogeneous inputs as a mixed stream of symbolic and numeric tokens, where a decoder-only autoregressive transformer provides global context and a conditional diffusion head generates numeric fields steered by task-specific tokens. Besides the new high SOTAs on structure prediction tasks, the model demonstrates state-of-the-art or competitive performance for the function-aware generation across domains: in materials, it achieves "conflicted" multi-property conditional generation, yielding 436 crystal candidates meeting triple constraints, including 11 with novel compositions; in chemistry, it sets new benchmarks on five property targets and conformer ensemble generation on GEOM; and in biology, it improves success in modeling protein induced fit (RMSD < 2 Å) by over 23-fold and enhances EC-conditioned enzyme design. Ablation studies and cross-domain transfer substantiate the benefits of joint discrete-continuous training, establishing UniGenX as a significant advance from prediction to controllable, function-aware generation.

cs.LG↗
arXiv2025

SafeCtrl: Region-Based Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress

The widespread deployment of text-to-image models is challenged by their potential to generate harmful content. While existing safety methods, such as prompt rewriting or model fine-tuning, provide valuable interventions, they often introduce a trade-off between safety and fidelity. Recent localization-based approaches have shown promise, yet their reliance on explicit ``concept replacement" can sometimes lead to semantic incongruity. To address these limitations, we explore a more flexible detect-then-suppress paradigm. We introduce SafeCtrl, a lightweight, non-intrusive plugin that first precisely localizes unsafe content. Instead of performing a hard A-to-B substitution, SafeCtrl then suppresses the harmful semantics, allowing the generative process to naturally and coherently resolve into a safe, context-aware alternative. A key aspect of our work is a novel training strategy using Direct Preference Optimization (DPO). We leverage readily available, image-level preference data to train our module, enabling it to learn nuanced suppression behaviors and perform region-guided interventions at inference without requiring costly, pixel-level annotations. Extensive experiments show that SafeCtrl significantly outperforms state-of-the-art methods in both safety efficacy and fidelity preservation. Our findings suggest that decoupled, suppression-based control is a highly effective and scalable direction for building more responsible generative models.

cs.CV↗
arXiv2025

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks

In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL.

cs.NI↗
arXiv2025

Manipulation of topology by electric field in breathing kagome lattice

Magnetic kagome lattices have attracted much attention recently due to the interplay of band topology with magnetism and electronic correlations, which give rise to a variety of exotic quantum states. A common structural distortion of the kagome lattice is the breathing mode, which can significantly influence the magnetism and band characteristics. However, the modulation of breathing mode and the associated topological phenomena remain rarely explored. Here, we demonstrate that the coupling of breathing modes with ferroelectricity, magnetism, and band topology in the M3X8 monolayer system enables electric field manipulation of topological spin structure and electronic states. The breathing mode mainly occurs in materials containing early 4d/5d transition metal elements and can be reversed or even suppressed via ferroelectric switching in low-barrier materials. Importantly, electric field-induced switching of the breathing mode can alter the chirality of the topological spin structure, or trigger a transition from a topological trivial insulator to a Chern insulator. This work paves the way for exploring novel physical phenomena driven by breathing modes in kagome materials.

cond-mat.mtrl-sci↗