arXiv subjects

Jiong Yang

Publications and source records attributed to Jiong Yang.

At least 19 recordsLinked to original sources

arXiv2026

Upper bounds for arithmetic functions over polynomial values via Chebotarev--Sato--Tate distributions

We establish logarithmic upper bounds for nonnegative multiplicative functions evaluated at values of multivariable polynomials. The polynomial contribution is encoded by the permutation character on the geometric irreducible components of the corresponding hypersurface, while the arithmetic contribution is described by a class function on a joint Chebotarev--Sato--Tate group. This yields a unified framework for symmetric-power coefficients of CM and non-CM modular forms, Dedekind zeta-function coefficients, and mixed automorphic--Galois weights. As a further application, we obtain quantitative divisibility results for Fourier coefficients along polynomial values, including explicit formulas in the full residual-image and Eisenstein congruence cases.

math.NT↗

arXiv2026

Pressure-induced Superconductivity in Thermoelectric Semiconductor Mg3Sb2

The intrinsic electronic structures of narrow bandgap thermoelectric (TE) materials serve as a platform for the investigation of coupling effects of quasi-particles under high pressure, enabling the exploration of emerging electronic and phonon transport, superconductivity, and topological transition. Here, we report the discovery of pressure-induced superconductivity in the TE semiconductor Mg3Sb2. Upon the increased pressure, the metallization occurs at 8.7 GPa, followed by a superconducting transition concomitant with a carrier-type crossover from p- to n-type. This phenomenon arises from a pressure-induced structural phase transition from the semiconducting P-3m1 to the metallic C2/m-I phase. The superconducting critical temperature (Tc) exhibits a dome-shaped pressure dependence, peaking at 3.3 K at 12.6 GPa. Combined theoretical calculations, high-pressure Raman spectroscopy, and X-ray diffraction (XRD) measurements reveal an additional structural transition above 20 GPa, yielding a distinct C2/m-II phase. Our findings establish the high-pressure phase diagram of Mg3Sb2, elucidate its pressure-dependent electronic properties, and provide valuable insights for future investigations of TE materials under high pressure.

cond-mat.supr-con↗

arXiv2026

Material-Property-Field-based Deep Neural Network in Hopfield Framework

Current deep neural networks (DNNs) used in materials modeling often lack explicit physical structure and clear analytical formulations tailored to material systems, which can limit their interpretability. In this work, we integrate Material Property Fields (MPF) with the Hopfield network architecture and propose an analytically structured DNN framework named mPFDNN. MPF provides a unified framework that represents physical properties of materials as an analytical field built upon pairwise interactions, rigorously respecting fundamental symmetries, while also enabling a physically legitimate decomposition of property distributions at the atomic level. Although the Hopfield model was originally developed for Ising-like systems, we show that its dynamical evolution strategy can be naturally extended to the MPF framework. By reformulating nonlinear interatomic interactions as "hidden neurons", MPF can be extended into a deep yet analytically tractable DNN architecture that progressively captures an increasingly connected interaction landscape. This framework also provides a unified perspective that connects linear expansions and nonlinear DNN architectures within a common interaction-based formulation. Extensive validation across diverse systems, including inorganic crystals, organic molecules, and aqueous solutions, and across multiple target properties, shows that mPFDNN achieves competitive predictive accuracy while offering a physically motivated framework for structure-property mapping in chemistry, physics, and materials science.

cond-mat.mtrl-sci↗

arXiv2026

Higher moments for symmetric powers of modular forms

Let $f$ be a cuspidal eigenform of weight $k$ on $\SL_2(\BZ)$ and let $\lambda_{\Sym^d f}(n)$ be the normalized Fourier coefficients of its $d$-th symmetric power lift. This paper establishes asymptotic formulas for the moments $\sum_{n\leq x}\lambda^l_{\Sym^d f}(n)$ for all positive integers $d$ and $l$. We also prove an asymptotic formula for the corresponding sum over the values of any positive definite binary quadratic form $Q$. Our results generalize and improve upon previous work, which was limited to small values of $d$ or $l$. The proofs rely on the decomposition of $\ell$-adic Galois representations and the analytic properties of the associated $L$-functions.

math.NT↗

arXiv2025

A Physics-Aware Attention LSTM Autoencoder for Early Fault Diagnosis of Battery Systems

Battery safety is paramount for electric vehicles. Early fault diagnosis remains a challenge due to the subtle nature of anomalies and the interference of dynamic operating noise. Existing data-driven methods often suffer from "physical blindness" leading to missed detections or false alarms. To address this, we propose a Physics-Aware Attention LSTM Autoencoder (PA-ALSTM-AE). This novel framework explicitly integrates battery aging laws (mileage) into the deep learning pipeline through a multi-stage fusion mechanism. Specifically, an adaptive physical feature construction module selects mileage-sensitive features, and a physics-guided latent fusion module dynamically calibrates the memory cells of the LSTM based on the aging state. Extensive experiments on the large-scale Vloong real-world dataset demonstrate that the proposed method significantly outperforms state-of-the-art baselines. Notably, it improves the recall rate of early faults by over 3 times while maintaining high precision, offering a robust solution for industrial battery management systems.

eess.SY↗

arXiv2025

Sensor-Space Based Robust Kinematic Control of Redundant Soft Manipulator by Learning

The intrinsic compliance and high degree of freedom (DoF) of redundant soft manipulators facilitate safe interaction and flexible task execution. However, effective kinematic control remains highly challenging, as it must handle deformations caused by unknown external loads and avoid actuator saturation due to improper null-space regulation - particularly in confined environments. In this paper, we propose a Sensor-Space Imitation Learning Kinematic Control (SS-ILKC) framework to enable robust kinematic control under actuator saturation and restrictive environmental constraints. We employ a dual-learning strategy: a multi-goal sensor-space control framework based on reinforcement learning principle is trained in simulation to develop robust control policies for open spaces, while a generative adversarial imitation learning approach enables effective policy learning from sparse expert demonstrations for confined spaces. To enable zero-shot real-world deployment, a pre-processed sim-to-real transfer mechanism is proposed to mitigate the simulation-to-reality gap and accurately characterize actuator saturation limits. Experimental results demonstrate that our method can effectively control a pneumatically actuated soft manipulator, achieving precise path-following and object manipulation in confined environments under unknown loading conditions.

cs.RO↗

arXiv2025

Efficient Certified Reasoning for Binarized Neural Networks

Neural networks have emerged as essential components in safety-critical applications -- these use cases demand complex, yet trustworthy computations. Binarized Neural Networks (BNNs) are a type of neural network where each neuron is constrained to a Boolean value; they are particularly well-suited for safety-critical tasks because they retain much of the computational capacities of full-scale (floating-point or quantized) deep neural networks, but remain compatible with satisfiability solvers for qualitative verification and with model counters for quantitative reasoning. However, existing methods for BNN analysis suffer from either limited scalability or susceptibility to soundness errors, which hinders their applicability in real-world scenarios. In this work, we present a scalable and trustworthy approach for both qualitative and quantitative verification of BNNs. Our approach introduces a native representation of BNN constraints in a custom-designed solver for qualitative reasoning, and in an approximate model counter for quantitative reasoning. We further develop specialized proof generation and checking pipelines with native support for BNN constraint reasoning, ensuring trustworthiness for all of our verification results. Empirical evaluations on a BNN robustness verification benchmark suite demonstrate that our certified solving approach achieves a $9\times$ speedup over prior certified CNF and PB-based approaches, and our certified counting approach achieves a $218\times$ speedup over the existing CNF-based baseline. In terms of coverage, our pipeline produces fully certified results for $99\%$ and $86\%$ of the qualitative and quantitative reasoning queries on BNNs, respectively. This is in sharp contrast to the best existing baselines which can fully certify only $62\%$ and $4\%$ of the queries, respectively.

cs.LG↗

arXiv2025

Global physics-informed neural networks (GPINNs): from local point-wise constraint to global nodal association

Recently, physics-informed neural networks (PINNs) and their variants have gained significant popularity as a scientific computing method for solving partial differential equations (PDEs), whereas accuracy is still its main shortcoming. Despite numerous development efforts, there is no literature demonstrating that these methods surpass classic numerical algorithms in solving the forward issue. In this paper, by analyzing the disparities between PINNs and traditional numerical methods based on mesh discretization, we investigate the underlying causes for the in adequate precision of PINNs and introduce a novel approach named global physics-informed neural networks (GPINNs). Inspired by the crucial concept of global nodal association in conventional numerical algorithms, GPINNs leverages the prior field distribution information from pre-trained PINNs to estimate the association weights between arbitrary nodes in space. GPINNs can not only be regarded as a meshless approach but also be demonstrated, both theoretically and in practical circumstances, to have the ability of second-order convergence when trained with equidistant nodes. Overall, GPINNs may be seen as an ideal approach to inheriting the merits of scientific machine learning (SciML) and conventional numerical computing, which also represent the first SciML algorithm to surpass standard numerical methods in terms of accuracy.

physics.comp-ph↗

arXiv2025

Glass-like anomalies and unconventional thermoelectric transport in chimney ladder crystals

Nowotny chimney ladder (NCL) crystals present physical properties in between the contrasting paradigms of ideal crystal and amorphous solid, making them promising candidates for thermoelectric applications due to their inherently low thermal conductivity. In this work, we report an extensive experimental characterization of the thermodynamic and thermoelectric transport properties of a large class of NCL materials, focusing on the intermetallic compound Ru$_2$Sn$_{3}$. We show that, despite their ordered crystalline structure, the heat capacity of these NCL compounds deviates from the Debye model at low temperatures and exhibits a boson-peak-like glassy anomaly in the range of $8$-$14$ K. By combining experimental measurements with density functional theory (DFT) and \emph{ab initio} molecular dynamics (AIMD) simulations, we attribute the microscopic origin of this glassy behavior to extremely low-energy optical phonons that universally emerge from the chimney ladder sublattice structure. Crucially, their coupling to acoustic phonons induces hybridization and avoided crossings, leading to strongly modified acoustic modes that directly contribute to the anomaly as well, similar to the case of other thermoelectric materials such as clathrates. Additionally, the measured thermal conductivity and the thermoelectric response present distinct anomalous glass-like features that strongly correlate with the dynamics of the low-lying optical phonons revealed by simulations. In particular, the electric resistivity displays an extended linear in $T$ behavior and an anomalously large $T^2$ contribution at low temperature. We propose a simple theoretical framework, based on electrons scattering with overdamped phononic modes, that qualitative explains both these features.

cond-mat.mtrl-sci↗

arXiv2025

Natural van der Waals canalization lens for non-destructive nanoelectronic circuit imaging and inspection

Optical inspection has long served as a cornerstone non-destructive method in semiconductor wafer manufacturing, particularly for surface and defect analysis. However, conventional techniques such as bright-field and dark-field scattering optics face significant limitations, including insufficient resolution and the inability to penetrate and detect buried structures. Atomic force microscopy (AFM), while offering higher resolution and precise surface characterization, is constrained by slow speed, limited to surface-level imaging, and incapable of resolving subsurface features. Here, we propose an approach that integrates the strengths of dark-field scattering optics and AFM by leveraging a van der Waals (vdW) canalization lens based on natural biaxial {\alpha}-MoO3 crystals. This method enables ultrahigh-resolution subwavelength imaging with the ability to visualize both surface and buried structures, achieving a spatial resolution of 15 nm and grating pitch detection down to 100 nm. The underlying mechanism relies on the unique anisotropic properties of {\alpha}-MoO3, where its atomic-scale unit cells and biaxial symmetry facilitate the diffraction-free propagation of both evanescent and propagating waves via a flat-band canalization regime. Unlike metamaterial-based superlenses and hyperlenses, which suffer from high plasmonic losses, fabrication imperfections, and uniaxial constraints, {\alpha}-MoO3 provides robust and aberration-free imaging in multiple directions. We successfully applied this approach to high-resolution inspection of buried nanoscale electronic circuits, offering unprecedented capabilities essential for next-generation semiconductor manufacturing.

physics.optics↗

arXiv2025

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity

Using machine learning (ML) to construct interatomic interactions and thus potential energy surface (PES) has become a common strategy for materials design and simulations. However, those current models of machine learning interatomic potential (MLIP) provide no relevant physical constrains, and thus may owe intrinsic out-of-domain difficulty which underlies the challenges of model generalizability and physical scalability. Here, by incorporating physics-informed Universal-Scaling law and nonlinearity-embedded interaction function, we develop a Super-linear MLIP with both Ultra-Small parameterization and greatly expanded expressive capability, named SUS2-MLIP. Due to the global scaling rooting in universal equation of state (UEOS), SUS2-MLIP not only has significantly-reduced parameters by decoupling the element space from coordinate space, but also naturally outcomes the out-of-domain difficulty and endows the potentials with inherent generalizability and scalability even with relatively small training dataset. The nonlinearity-enbeding transformation for interaction function expands the expressive capability and make the potentials super-linear. The SUS2-MLIP outperforms the state-of-the-art MLIP models with its exceptional computational efficiency especially for multiple-element materials and physical scalability in property prediction. This work not only presents a highly-efficient universal MLIP model but also sheds light on incorporating physical constraints into artificial-intelligence-aided materials simulation.

cond-mat.mtrl-sci↗

arXiv2024

Automated production of batched unclonable micro-patterns anti-counterfeiting labels with strong robustness and rapid recognition speed

Anti-counterfeiting technologies are indeed crucial for information security and protecting product authenticity. Traditional anti-counterfeiting methods have their limitations due to their clonable nature. Exploring new technologies, particularly those based on pixel-level textures is a promising avenue to address the clonable issue due to high encoding capacity. However, research in this field is still in its infancy. This work introduces a new fluorescent anti-counterfeiting label technology with four key characteristics: efficient laser etching, high-throughput fabrication and segmentation, robustness aided by data augmentation, and an exceptionally high recognition speed. To be specific, the etching achieves a speed of 1,200 labels/3s, the high throughput yields a rate of 2,400 labels/4 min, and a total count of 51,966 labels. The number of labels is further augmented to 5,196,600 by implementing arbitrary rotation and brightness variation to enhance the robustness in the recognition procedure. We divide these labels into 44 categories based on differences in patterns. Utilizing machine learning methods, we have achieved a total recognition (including extraction and search process) time per label averaging 421.96 ms without classification, and 40.13 ms with classification. Specifically, the search process with classification is nearly fiftieth times shorter than the non-classification method, reaching 8.52 milliseconds in average. The overall recognition time is much faster than previous works, and achieve an accuracy over 98.7%. This work significantly increases the practicality of pixel-level anti-counterfeiting labels.

cond-mat.mtrl-sci↗

arXiv2024

Automated high-resolution backscattered-electron imaging at macroscopic scale

Scanning electron microscopy (SEM) has been widely utilized in the field of materials science due to its significant advantages, such as large depth of field, wide field of view, and excellent stereoscopic imaging. However, at high magnification, the limited imaging range in SEM cannot cover all the possible inhomogeneous microstructures. In this research, we propose a novel approach for generating high-resolution SEM images across multiple scales, enabling a single image to capture physical dimensions at the centimeter level while preserving submicron-level details. We adopted the SEM imaging on the AlCoCrFeNi2.1 eutectic high entropy alloy (EHEA) as an example. SEM videos and image stitching are combined to fulfill this goal, and the video-extracted low-definition (LD) images are clarified by a well-trained denoising model. Furthermore, we segment the macroscopic image of the EHEA, and area of various microstructures are distinguished. Combining the segmentation results and hardness experiments, we found that the hardness is positively correlated with the content of body-centered cubic (BCC) phase, negatively correlated with the lamella width, and the relationship with the proportion of lamellar structures was not significant. Our work provides a feasible solution to generate macroscopic images based on SEMs for further analysis of the correlations between the microstructures and spatial distribution, and can be widely applied to other types of microscope.

cond-mat.mtrl-sci↗

arXiv2024

When is the Four-phonon Effect in Half-Heusler Materials more Pronounced?

Suppressed three-phonon scattering processes have been considered to be the direct cause of materials exhibiting significant higher-order four-phonon interactions. However, after calculating the phonon-phonon interactions of 128 Half-Heusler materials by high-throughput, we find that the acoustic phonon bandwidth dominates the three-phonon and four-phonon scattering channels and keeps them roughly in a co-increasing or decreasing behavior. The $aao$ and $aaa$ three-phonon scattering channels in Half-Heusler materials are weakly affected by the acoustic-optical gap and acoustic bunched features respectively only when acoustic phonon bandwidths are close. Finally, we found that Half-Heusler materials with smaller acoustic bandwidths tend to have a more pronounced four-phonon effect, although three-phonon scattering may not be significantly suppressed at this time.

cond-mat.mtrl-sci↗

arXiv2024

The role of lattice thermal conductivity suppression by dopants from a holistic perspective

Dopants play an important role in improving electrical and thermal transport. In the traditional perspective, a dopant suppresses lattice thermal conductivity kL by adding point defect (PD) scattering term to the phonon relaxation time, which has been adopted for decades. In this study, we propose an innovative perspective to solve the kL of defective systems-the holistic approach, i.e., treating dopant and matrix as a holism. This approach allows us to handle the influences from defects explicitly by the calculations of defective systems, about their changed phonon dispersion, phonon-phonon and electron-phonon interaction, etc, due to the existence of dopants. The kL reduction between defective MxNb1-xFeSb (M=V, Ti) and NbFeSb is used as an example for the holistic approach, and comparable results with experiments are obtained. It is notable that light elemental dopants also induced the avoided-crossing behavior. It can be further rationalized by a one-dimensional atomic chain model. The mass and force constant imbalance generally generates the avoided-crossing phonons, mathematically in a similar way as the coefficients in traditional PD scattering, but along a different direction in kL reduction. Our work provides another perspective for understanding the mechanism of dopants influence in material's thermal transport.

cond-mat.mtrl-sci↗

arXiv2024

Screening of half-Heuslers with temperature-induced band convergence and enhanced thermoelectric properties

Enhancing band convergence is an effective way to optimize the thermoelectric (TE) properties of materials. However, the temperature-induced band renormalization is commonly ignored. By employing the recently-developed electron-phonon renormalization (EPR) method, the nature of band renormalization in half-Heusler (HH) compounds TiCoSb and NbFeSb is revealed, and the key factors for temperature-induced conduction band convergence in HH are found out. Using these as the screening criteria, 3 out of 274 HHs (TiRhBi, TiPtSn, NbPtTl) are then stood out from our MatHub-3d database. Taking TiPtSn as the example, it shows the conduction band convergence at mid-high temperature, and further resulting in enhanced Seebeck coefficient S: e.g., at 600 K with electron concentration 10^20 cm^-3, the predicted S with and without renormalized band is 352.83 uV/K and 289.52 uV/K, respectively. Herein, the former is closer to our measurement value of 338.79 uV/K. Besides, the effective masses obtained from calculation and experiment are both enlarged with temperature, indicating the existence of band convergence. Our work demonstrates for the first time the significance of adding the temperature effect on electronic structure in the design of potential high-performance TE materials.

cond-mat.mtrl-sci↗

arXiv2024

Formally Certified Approximate Model Counting

Approximate model counting is the task of approximating the number of solutions to an input Boolean formula. The state-of-the-art approximate model counter for formulas in conjunctive normal form (CNF), ApproxMC, provides a scalable means of obtaining model counts with probably approximately correct (PAC)-style guarantees. Nevertheless, the validity of ApproxMC's approximation relies on a careful theoretical analysis of its randomized algorithm and the correctness of its highly optimized implementation, especially the latter's stateful interactions with an incremental CNF satisfiability solver capable of natively handling parity (XOR) constraints. We present the first certification framework for approximate model counting with formally verified guarantees on the quality of its output approximation. Our approach combines: (i) a static, once-off, formal proof of the algorithm's PAC guarantee in the Isabelle/HOL proof assistant; and (ii) dynamic, per-run, verification of ApproxMC's calls to an external CNF-XOR solver using proof certificates. We detail our general approach to establish a rigorous connection between these two parts of the verification, including our blueprint for turning the formalized, randomized algorithm into a verified proof checker, and our design of proof certificates for both ApproxMC and its internal CNF-XOR solving steps. Experimentally, we show that certificate generation adds little overhead to an approximate counter implementation, and that our certificate checker is able to fully certify $84.7\%$ of instances with generated certificates when given the same time and memory limits as the counter.

cs.LO↗

arXiv2024

Magnetic structure and Ising-like antiferromagnetism in the bilayer triangular lattice compound NdZnPO

The complex interplay of spin frustration and quantum fluctuations in low-dimensional quantum materials leads to a variety of intriguing phenomena. This research focuses on a detailed analysis of the magnetic behavior exhibited by NdZnPO, a bilayer spin-1/2 triangular lattice antiferromagnet. The investigation employs magnetization, specific heat, and powder neutron scattering measurements. At zero field, a long-range magnetic order is observed at $T_{\rm N}=1.64~\rm K$. Powder neutron diffraction experiments show the Ising-like magnetic moments along the $c$-axis, revealing a stripe-like magnetic structure with three equivalent magnetic propagation vectors. Application of a magnetic field along the $c$-axis suppresses the antiferromagnetic order, leading to a fully polarized ferromagnetic state above $B_{\rm c}=4.5~\rm T$. This transition is accompanied by notable enhancements in the nuclear Schottky contribution. Moreover, the absence of spin frustration and expected field-induced plateau-like phases are remarkable observations. Detailed calculations of magnetic dipolar interactions revealed complex couplings reminiscent of a honeycomb lattice, suggesting the potential emergence of Kitaev-like physics within this system. This comprehensive study of the magnetic properties of NdZnPO highlights unresolved intricacies, underscoring the imperative for further exploration to unveil the underlying governing mechanisms.

cond-mat.str-el↗