arXiv subjects

Haowei Xu

Publications and source records attributed to Haowei Xu.

At least 19 recordsLinked to original sources

arXiv2026

Coupled-cluster molecular properties across the main group that extrapolate beyond training size

Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whereas density-functional theory is affordable yet systematically biased. We resolve this trade-off with a single equivariant network, MEHnet-MG, that predicts an effective one-electron Hamiltonian from one inexpensive B3LYP/def2-SVP calculation and derives a broad suite of properties from it (energy, optical gap, dipole, quadrupole, polarizability, Mulliken atomic charges, and Mayer bond orders) at coupled-cluster accuracy across nine main-group elements, including the under-served phosphorus, sulfur, and chlorine chemistries. The model is trained on a new in-house dataset of multi-property labels computed at the CCSD(T) level for all nine elements. On a held-out test set, it reduces the error of every property by a factor of 3.8 to 230 relative to semi-local, hybrid, and double-hybrid DFT (referenced to composite CCSD(T)/cc-pVTZ; Methods), while adding only ~25 ms wall time per molecule, delivering coupled-cluster-quality predictions at the cost of a single DFT calculation. Critically, deriving every property from a predicted Hamiltonian rather than pooling per-atom features builds the correct size-scaling into the model architecture: on pi-conjugated oligothiophenes it matches finite-field CCSD polarizability and the EOM-CCSD optical gap to ~2% at the largest sizes where those references remain affordable (44 and 37 atoms, where a single CCSD field point already costs ~500x the model's entire inference) and extrapolates the corrected trends to 58-atom chains, a regime where pooling-based architectures fail by construction. Accurate extrapolation is therefore set by the model's inductive bias rather than by the training data.

physics.chem-ph↗

arXiv2026

Nonresonant optomechanical control of structural phases

Optical tweezers demonstrate how light can exert forces to trap, repel, and manipulate microscopic particles without absorption. Recent theory has suggested that such forces can extend beyond particle manipulation to drive structural phase transitions in solids. Here we apply this optomechanical principle to tin selenide (SnSe), a material where proximity to several different structural phases gives rise to its high thermoelectric figure of merit and makes it a candidate for a switchable topological crystalline insulator. Whereas the force for standard optical tweezers arises from a gradient in the intensity of a light field, the optomechanical force is mediated by a gradient in the dielectric constant as a function of phonon coordinate. Unlike conventional methods that rely on resonant excitation and absorption through the imaginary part of the dielectric function, this approach operates dispersively through the real part and can be directly driven by Raman processes, enabling selective transitions with reduced energy cost and ultrafast response. Using time-domain Raman scattering, we show that above a critical mid-infrared field strength the $A_g$ Raman modes disappear abruptly without softening, signaling the formation of a new structural phase. This phase, distinct from those induced by heating or carrier excitation, exhibits large-amplitude and long-lived modulations in its optical response. Complementing this observation, we show also evidence for an equivalent DC-field-driven structural phase transformation to a higher symmetry phase, as observed by atom probe tomography. Our study demonstrates the concept of nonresonant optomechanical phase control and defines novel opportunities for synthesizing hidden structural phases with unique functional properties.

physics.optics↗

arXiv2026

Exotic Cooperative Quantum Optics of Moire Exciton Superlattices

The unique properties of two-dimensional moire systems have been widely studied from many perspectives. However, relatively little work has explored how the real space structure of the moire systems can directly engender novel properties and functionalities. In this work, we exploit the feature that moire excitons naturally form an ordered superlattice with a lattice constant comparable to the wavelength of the resonant light, which enables intriguing cooperative optical responses. Particularly, we show that the collective moire exciton states can have either strongly enhanced (superradiant) or suppressed (subradiant) radiative decay rate, depending on their in-plane wavevector. These super- and subradiant states can be efficiently switched by a gate-induced electric field gradient. Moreover, the cooperative transmittance $T$ of the nanometer-thick moire system can be switched from $T \approx 0$ (opaque) to $T \approx 1$ (transparent) with less than $2~\%$ heterostrain or a $1^{\circ}$ adjustment in the twist angle $\theta$. These features are robust against non-radiative losses and inhomogeneity, making the moire system a highly versatile platform for cooperative quantum optics with potential applications in e.g., single photon storage and switching.

cond-mat.mtrl-sci↗

arXiv2025

Prediction of Ambient-Pressure High-Temperature Superconductivity in Doped Transition-Metal Hydrides

The search for conventional superconductors with high transition temperatures ($T_c$) has largely focused on intrinsically metallic compounds. In this work, we explore the potential of intrinsically non-metallic compounds to exhibit high-$T_c$ superconductivity under ambient pressure through carrier doping. We identify $\rm MgAlFeH_6$, a representative of carrier-doped transition-metal hydrides like $\rm Mg_2FeH_6$, as a promising example with a predicted $T_c \approx 130~\rm K$. We propose that the average projected electron density of states, defined as the geometric mean of the total and hydrogen-projected density of states at the Fermi level, serves as a simple and computationally inexpensive indicator of high-$T_c$ behavior. We also highlight the tradeoff between high-$T_c$ and dynamic stability, both of which depend on the electron density of states. Our findings thus expand the pool of potential superconducting materials and offer a practical route for accelerating the discovery of superconductors suitable for real-world applications.

cond-mat.supr-con↗

arXiv2025

OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities

Improving visual text synthesis has long been a challenging and evolving frontier for image generation models. While recent state-of-the-art (SOTA) models have made remarkable strides in text generation capabilities, existing benchmarks inadequately assess their true performance due to narrow scope (scene text and posters only), isolated evaluation (T2I generation or editing separately), and insufficient difficulty (lacking challenging scenarios). To bridge this gap, we pioneer the unification of text-centric T2I generation, text editing, and OCR-related image-to-image translation to evaluate a model's holistic visual text synthesis abilities, i.e., OCR generative capabilities. Accordingly, we propose OCRGenBench, the most comprehensive benchmark to date for evaluating these abilities. OCRGenBench covers five common text categories and 33 OCR generative tasks, encompassing T2I generation, text editing, and other image-to-image OCR tasks (e.g., document dewarping and handwriting removal). The benchmark includes 1,060 human-annotated samples consisting of instruction-image-GT triplets, deliberately featuring high text density, diverse generation scales, varied aspect ratios, and bilingual content to capture real-world complexity. Furthermore, we introduce OCRGenScore, a unified metric integrating text accuracy, aesthetic quality, and instruction following. Extensive experiments on 19 cutting-edge generative models reveal that most score below 60/100. Our analysis exposes critical, previously overlooked limitations, including poor text localization, unintended content modifications, and failures with dense or small-scale text. We hope OCRGenBench establishes a robust standard to evaluate OCR generative capabilities, driving the evolution of reliable visual text synthesis. The benchmark and evaluation code are available at https://github.com/NiceRingNode/Awesome-Generative-Models-for-OCR.

cs.CV↗

arXiv2025

Ultrafast switchable polar and magnetic orders by nonlinear light-matter interaction

An outstanding challenge in materials science and physics is the harnessing of light for switching charge order in e.g., ferroelectrics. Here we propose a mechanism through which electrons in ferroelectric bilayers excited with light cause ionic structural transitions. Using perturbation theory within a many-body formalism, we show that the ionic coupling is mediated by a resonant change in electronic occupation functions, ultimately governed by the quantum geometric tensor (QGT) of the ground state. Furthermore, we show that such transitions are generally accompanied by multiferroic order switching. We demonstrate two examples of light-induced structural and polarization switching under this mechanism using first-principle calculations on bilayer CrI$_{3}$ and MoTe$_{2}$. We show that the two materials can switch between atomic stackings with a light intensity threshold of only 10-100 GW/cm$^2$, a value 1-3 orders of magnitude lower than that required by direct light-ion coupling thanks to the superior efficiency of resonant light-electron coupling. Since such switching is fast, highly controllable, contactless, and reversible, it is promising for use in optically controlled nonvolatile memory, nanophotonics and polar electronics.

cond-mat.mtrl-sci↗

arXiv2025

Strong Long-Wave Infrared Optical Response in a Topological Semiconductor with a Mexican Hat Band Structure

Light sources and photodetectors operating in the far- to mid-infrared (FIR/MIR) band ($8$-$12~\rm \mu m$, $0.1$-$0.15~\rm eV$) remain relatively poorly developed compared to their counterparts operating in the visible and near-infrared ranges, despite extensive application potential for thermal imaging, standoff sensing, and other technologies. This is attributable in part to the lack of narrow-gap materials ($<0.1~\rm eV$) with high optical gain and absorption. In this work, a narrow-gap semiconductor, $\rm Pb_{0.7}Sn_{0.3}Se$, is demonstrated to exhibit an optical response $>10\times$ larger than that of $\rm Hg_{x}Cd_{1-x}Te$ (MCT), the dominant material for FIR/MIR photodetectors. A previous theoretical investigation indicated that chalcogen $p$ and metal $d$ band inversion in this material creates a Mexican hat band structure (MHBS), which results in a dramatic increase in the joint density of states at the optical transition edge compared to typical semiconductors. This prediction is experimentally validated here using single-crystal specimens of $\rm Pb_{0.7}Sn_{0.3}Se$ measured using temperature-dependent spectroscopic ellipsometry over a wavelength range of $1.7$-$20~\rm \mu m$ ($0.73$-$0.062~\rm eV$). These measurements demonstrate a large enhancement in extinction coefficient and refractive index characteristic of a MHBS in the vicinity of the absorption edge, in agreement with theoretical predictions. The realization of topological semiconductors with a MHBS is expected to lead to high-efficiency detectors operating in the FIR/MID range.

physics.optics↗

arXiv2025

Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference

With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial. Previous works mainly focus on obtaining social relationships of news via retweets, limiting the accurate detection when real cascades are inaccessible. Given the proven assessment of the spreading influence of events, this paper proposes a method called HML (Complex Heterogeneous Multimodal Fake News Detection method via Latent Network Inference). Specifically, an improved social latent network inference strategy is designed to estimate the maximum likelihood of news influences under the same event. Meanwhile, a novel heterogeneous graph is built based on social attributes for multimodal news under different events. Further, to better aggregate the relationships among heterogeneous multimodal features, this paper proposes a self-supervised-based multimodal content learning strategy, to enhance, align, fuse and compare heterogeneous modal contents. Based above, a personalized heterogeneous graph representation learning is designed to classify fake news. Extensive experiments demonstrate that the proposed method outperforms the SOTA in real social media news datasets.

cs.MM↗

arXiv2025

Linear and Nonlinear Edelstein Effects in Chiral Topological Semimetals

Recently, there has been growing interest in achieving on-demand control of magnetism through electrical and optical means. In this work, we provide first-principles predictions for the linear and nonlinear Edelstein effects (LEE and NLEE) in the chiral topological semimetal CoSi. The LEE and NLEE represent first- and second-order magnetic responses to external electric fields, enabling precise manipulation of magnetization via electrical and optical methods. We demonstrate that although both LEE and NLEE require time-reversal symmetry breaking, they can still be realized in non-magnetic materials, as time-reversal symmetry can be spontaneously broken by heat and dissipation, according to the second law of thermodynamics. Meanwhile, due to different inversion symmetry selection rules, the LEE and NLEE manifest opposite and identical signs in the two enantiomers of CoSi, respectively. We further quantify the magnitude of LEE and NLEE, showing that electrically or optically induced magnetization can reach 10 Bohr magneton per unit cell when the external electric field strength is comparable with the internal atomic electric field, which is on the order of 1 V/A. Our work offers a systematical approach for predicting the electrical and optical control of magnetism in real materials, paving the way for potential applications in areas such as spintronics and magnetic memories.

cond-mat.mtrl-sci↗

arXiv2024

Collective Creation of Intimacy: Exploring the Cosplay Commission Practice within the Otome Game Community in China

Cosplay commission (cos-commission) is a new form of commodified intimate relationship within the Otome game community in China. To explore the motivations, practices, experiences, and challenges, we conducted semi-structured interviews with 15 participants in different roles. Our findings reveal that cos-commission, as a hybrid activity, provides participants with a chance to collaboratively build meaningful connections. It also offers a pathway for personal exploration and emotional recovery. However, the vague boundary between performative roles and intimate interactions can give rise to unexpected negative outcomes, such as attachment-driven entanglements and post-commission ``withdrawal symptoms.'' While digital platforms facilitate communication in cos-commissions, they often lack sufficient safeguards. This preliminary work provides insights into the formation process of hybrid intimate relationship and its potential to foster personalized, long-term support for mental well-being, and reveals potential privacy and safety challenges.

cs.HC↗

arXiv2024

Non-Hermitian Spin-Spin Interaction Mediated by Chiral Phonons

Non-Hermiticity and chirality are two fundamental properties known to give rise to various intriguing phenomena. However, the interplay between these properties has been rarely explored. In this work, we bridge this gap by introducing an off-diagonal non-Hermitian spin-spin interaction mediated by chiral phonons. This interaction arises from the spin-selectivity due to the locking between phonon momentum and angular momentum in chiral materials. The resulting non-Hermitian interaction mediated by the vacuum field of chiral phonons can reach the kHz range for electron spins and can be further enhanced by externally driven mechanical waves, potentially leading to observable effects in the quantum regime. Moreover, the long-range nature of phonon-mediated interactions enables the realization of the long-desired non-Hermitian interaction among multiple spins. The effect proposed in this work may have wide-ranging applications in cascaded quantum systems, non-Hermitian many-body physics, and non-Hermitian cooling.

quant-ph↗

arXiv2024

Nonresonant Raman control of ferroelectric polarization

Important advances have recently been made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light-matter interactions via the imaginary part of the dielectric function through above-bandgap or resonant mode excitation. Nonresonant Raman excitation of coherent modes has been experimentally observed and proposed for dynamic material control, but the resulting atomic excursion has been limited to perturbative levels. Here, this challenge is overcome by employing nonresonant ultrashort pulses with low photon energies well below the bandgap. Using mid-infrared pulses, ferroelectric reversal is induced in lithium niobate, and the large-amplitude mode displacements are characterized through femtosecond stimulated Raman scattering and second harmonic generation. This approach, validated by first-principle calculations, defines a novel method for synthesizing hidden phases with unique functional properties and manipulating complex energy landscapes at reduced energy consumption and ultrafast speeds.

physics.optics↗

arXiv2024

Comment on "High-Power Collective Charging of a Solid-State Quantum Battery"

In the Letter [Physical Review Letters 120, 117702 (2018)], Ferraro \emph{et al.} claimed a quantum advantage in the Dicke quantum battery (QB), whereby $N$ two-level systems (TLS) are coupled to a common photonic mode of a cavity. They argued that compared with the so-called Rabi QB, the Dicke QB exhibits a $\sqrt{N}$ quantum enhancement in the charging power because of the entanglement created by the common photonic mode. In this Comment, however, we demonstrate that the apparent $\sqrt{N}$ enhancement actually comes from the stronger cavity electric (or magnetic) field under the setup discussed in [Physical Review Letters 120, 117702 (2018)]. This is a trivial classical effect, and there is no true "quantum advantage" in the charging power of the Dicke QB. While somewhat similar questions regarding the origin of the claimed "quantum advantage" have been raised before, here we would like to make it clear that the $\sqrt{N}$ enhancement in [Physical Review Letters 120, 117702 (2018)] is purely a classical effect, not attributable to quantum entanglement or collective phenomena. Therefore, we believe that using the term "quantum battery" in this context may be inappropriate and misleading.

quant-ph↗

arXiv2024

Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review

With the rapid development of artificial intelligence (AI), large language models (LLMs) have shown strong capabilities in natural language understanding, reasoning, and generation, attracting amounts of research interest in applying LLMs to health and medicine. Critical care medicine (CCM) provides diagnosis and treatment for critically ill patients who often require intensive monitoring and interventions in intensive care units (ICUs). Can LLMs be applied to CCM? Are LLMs just like stochastic parrots or ICU experts in assisting clinical decision-making? This scoping review aims to provide a panoramic portrait of the application of LLMs in CCM. Literature in seven databases, including PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE Xplore, and ACM Digital Library, were searched from January 1, 2019, to June 10, 2024. Peer-reviewed journal and conference articles that discussed the application of LLMs in critical care settings were included. From an initial 619 articles, 24 were selected for final review. This review grouped applications of LLMs in CCM into three categories: clinical decision support, medical documentation and reporting, and medical education and doctor-patient communication. LLMs have advantages in handling unstructured data and do not require manual feature engineering. Meanwhile, applying LLMs to CCM faces challenges, including hallucinations, poor interpretability, bias and alignment challenges, and privacy and ethics issues. Future research should enhance model reliability and interpretability, integrate up-to-date medical knowledge, and strengthen privacy and ethical guidelines. As LLMs evolve, they could become key tools in CCM to help improve patient outcomes and optimize healthcare delivery. This study is the first review of LLMs in CCM, aiding researchers, clinicians, and policymakers to understand the current status and future potentials of LLMs in CCM.

cs.AI↗

arXiv2024

Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy

Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work, we developed a unified ML method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties. As case studies, we apply the model to aromatic compounds and semiconducting polymers on both ground state and excited state properties, demonstrating its accuracy and generalization capability to complex systems that are hard to calculate using CCSD(T)-level methods.

physics.chem-ph↗

arXiv2023

Blind quantum machine learning with quantum bipartite correlator

Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes. In this work, we introduce novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm. Our protocols have reduced communication overhead while preserving the privacy of data from untrusted parties. We introduce robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques. We then validate the effectiveness of the proposed protocols through complexity and privacy analysis. Our findings pave the way for advancements in distributed quantum computing, opening up new possibilities for privacy-aware machine learning applications in the era of quantum technologies.

quant-ph↗

arXiv2023

Ferroelastic twin wall mediated ferro-flexoelectricity and bulk photovoltaic effect in SrTiO$_3$

Ferroelastic twin walls in nonpolar materials can give rise to a spontaneous polarization due to symmetry breaking. Nevertheless, the bi-stable polarity of twin walls and its reversal have not yet been demonstrated. Here, we report that the polarity of SrTiO$_3$ twin walls can be switched by ultra-low strain gradient. Using first-principles-based machine-learning potential, we demonstrate that the twin walls can be deterministically rotated and realigned in specific directions under strain gradient, which breaks the inversion symmetry of a sequence of walls and leads to a macroscopic polarization. The system can maintain polarity even after the strain gradient is removed. As a result, the polarization of twin walls can exhibit ferroelectric-like hysteresis loop upon cyclic bending, namely ferro-flexoelectricity. Finally, we propose a scheme to experimentally detect the polarity of twin wall by measuring the bulk photovoltaic responses. Our findings suggest a twin-wall-mediated ferro-flexoelectricity in SrTiO$_3$, which could be potentially exploited as functional elements in nano-electronic devices design.

cond-mat.mtrl-sci↗

arXiv2023

Precise Fermi-level engineering in a topological Weyl semimetal via fast ion implantation

The precise controllability of the Fermi level is a critical aspect of quantum materials. For topological Weyl semimetals, there is a pressing need to fine-tune the Fermi level to the Weyl nodes and unlock exotic electronic and optoelectronic effects associated with the divergent Berry curvature. However, in contrast to 2D materials, where the Fermi level can be controlled through various techniques, the situation for bulk crystals beyond laborious chemical doping poses significant challenges. Here, we report the meV-level ultra-fine-tuning of the Fermi level of bulk topological Weyl semimetal TaP using accelerator-based high-energy hydrogen implantation and theory-driven planning. By calculating the desired carrier density and controlling the accelerator profiles, the Fermi level can be fine-tuned from 5 meV to only $\sim$0.5 meV (DFT calculations) away from the Weyl nodes. The Weyl nodes are preserved, while the carrier mobility is largely retained. Our work demonstrates the viability of this generic approach to tune the Fermi level in semimetal systems and could serve to achieve property fine-tuning for other bulk quantum materials with ultrahigh precision.

cond-mat.mtrl-sci↗