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Yinan Wang

Publications and source records attributed to Yinan Wang.

At least 19 recordsLinked to original sources
arXiv2026

All-Electron Single-Atom Reference Correction for Absolute Transition Energies in Fixed-Reference PAW-XCH Calculations

The transition energy of a core-loss spectrum comprises a transferable atomic core-reference contribution and a material-dependent response that determines chemical shifts. Density functional theory calculations commonly underestimate absolute transition energies. In VASP, the same PAW dataset is used for the ground and core-excited occupations, retaining the ground-state atomic reference when the core occupation changes and thereby omitting the associated all-electron atomic reference change. We evaluate this change using self-consistent single-atom calculations and construct a residual atomic reference correction from the valence reference change and isolated-atom total-energy response. Comparisons with an independent ultrasoft-pseudopotential implementation demonstrate numerical consistency of the all-electron reference for K, L1, and L2,3 core holes, while representative corrected spectra approach the experimental absolute energy scale. For a fixed element, edge, and core-hole scheme, the atomic reference terms cancel from energy differences, showing that same-element chemical shifts are governed by the double difference of the supercell total energy. At the Al and Si L2,3 edges, the near-edge spectral shape additionally depends strongly on the PAW representation of low-lying 3d-like unoccupied states.

cond-mat.mtrl-sci↗
arXiv2026

FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models

Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available training datasets, while zero-shot methods tend to produce artifacts and require time-consuming test-time optimization. To address these issues, we propose FreeShadow, a training-free shadow removal method built upon pretrained diffusion models, which exploits diffusion priors for shadow removal without any training or optimization. For illumination recovery, we propose an illumination transfer attention (ITA), which re-weights the self-attention maps in diffusion model to transfer illumination cues from non-shadow to shadow regions. For content preservation, we analyze the effects of illumination variations on self-attention maps and latent high-frequency features in diffusion model, and selectively preserve illumination-invariant components to maintain content fidelity while suppressing residual shadows. We further propose local texture-preserving relighting (LTPR) to mitigate local texture misalignment caused by VAE compression. Extensive experiments demonstrate that our method achieves strong generalization and produces realistic shadow-free images.

cs.CV↗
arXiv2026

Quantum Safe-Set Bayesian Optimization for Quality Improvement in Fuselage Assembly

Recent efforts in smart manufacturing have enhanced aerospace fuselage assembly processes, particularly by innovating shape adjustment techniques to minimize dimensional gaps between assembled sections. Existing approaches have shown promising results but face the issue of low sample efficiency from the manufacturing systems. It arises from the limitation of the classical Monte Carlo method when uncovering the mean response from a distribution. In contrast, recent work has shown that quantum algorithms can achieve the same level of estimation accuracy with significantly fewer samples than the classical Monte Carlo method from distributions. Therefore, we can adopt the estimation of the quantum algorithm to obtain the estimation from real physical systems (distributions). Motivated by this advantage, we propose a Quantum Bayesian Optimization (QBO) framework for precise shape control during assembly to improve the sample efficiency in manufacturing practice. Specifically, this approach utilizes a quantum oracle, based on finite element analysis (FEA)-based models or surrogate models, to acquire a more accurate estimation of the environment response with fewer queries for a certain input. QBO employs an Upper Confidence Bound (UCB) as the acquisition function to strategically select input values that are most likely to maximize the objective function. It has been theoretically proven to require much fewer samples while maintaining comparable optimization results. In the case study, force-controlled actuators are applied to one fuselage section to adjust its shape and reduce the gap to the adjoining section. Experimental results demonstrate that QBO achieves significantly lower dimensional error and uncertainty compared to classical methods, particularly using the same queries from the simulation.

cs.LG↗
arXiv2026

Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development

AI-native biotechnology companies are often designed by copying human biotech org charts into agent roles. We argue for a different abstraction: a Company World Model, defined as a persistent asset-to-value state representation with transition models, explicit value functions, planning, and updating across scientific, regulatory, BD, commercial, financial, and execution constraints. We introduce a dry-lab benchmark for testing whether AI-agent organizations should mimic departments or operate around such a world model. The benchmark contains 45 retrospective public-information decision cases with strict time cutoffs, hidden outcomes, common schemas, automatic scoring, and blinded pairwise judging. We compare human-org-mimic, stronger human-org-mimic-plus, AI-native asset-centric, and AI-native value-conversion architectures. The value-conversion architecture is a prompt-level approximation of a Company World Model: a Live Asset Value Record updated by Deal, Approval, Revenue, and Investment Arbiter loops. Under a success function defined by external BD, regulatory approval and launch, and revenue discipline, it achieved the highest automatic value-conversion score and was strongly preferred over the original baselines by value-specific blinded judges. Stress tests narrowed the claim: a stronger human baseline remained competitive, and a neutral judge did not show robust value-conversion dominance. Codex-only mechanistic ablations suggest that Revenue Room, Deal Room, and Approval Room carry useful work under the target objective. The central finding is objective-sensitive: departments may remain useful governance views, but the core AI-native operating primitive should be a shared, predictive asset-to-value state rather than a static human org chart. The study is dry-lab only and does not establish real-world drug success, clinical benefit, or revenue prediction accuracy.

cs.AI↗
arXiv2026

Thin Film AlN Microbolometer for Very Long-Wave Infrared Detection

We demonstrate a suspended thin-film aluminum nitride (AlN) microbolometer for narrowband very long-wave infrared detection. The device uses a 100-nm-thick AlN membrane suspended above a Pt back reflector by a 1-um air gap. Resonant absorption is set by the AlN transverse optical phonon near 15.4 um and is strengthened by suspension above the reflector. A periodic perforation pattern reduces membrane thermal mass and enhances absorption without further thinning the film. DC resistance measurements under tunable infrared illumination verify bolometric operation, and the measured spectral response follows the absorption profile expected from spectroscopic measurement of passive devices. Narrowband response is observed in the 14--18 um range, with peak responsivity of 920.8 ppm/mW at 15.48 um. This platform can enable compact wavelength-selective thermal detectors for multispectral imaging, on-chip infrared spectroscopy, and chemical sensing.

physics.optics↗
arXiv2026

AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation

AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a different hypothesis in drug-asset valuation: for knowledge-intensive scientific decisions, the limiting factor is often the evidence substrate the agent can access. We run a controlled three-arm ablation on a production valuation agent: A is a plain web-only LLM analyst, B adds public structured tools plus a 14-dimension valuation playbook, verifier, objectivity policy and red-team, and C adds the proprietary Noah AI corpus of curated pipeline, trial and deal intelligence. Across a 13-asset stratified benchmark, B improves calibration and audit discipline: tier-in-range accuracy rises from 0.80 to 0.89 and objectivity from 3.16 to 3.30. But B does not remove the factual ceiling. Under capability-superset accounting, A and B recover only 0.25 and 0.38 of the curated gold competitive record, while C recovers 0.96; on the curated long-tail subset, C reaches 0.93 vs. 0.26/0.30. Raw blind-panel decision quality is similar for A and B (7.01 vs. 6.96), so we introduce completeness-aware decision utility: informed decision-quality = decision-quality x gold-coverage. On this metric, C reaches 7.43 vs. 1.76/2.57 for A/B. Even a perfect non-proprietary-data report would be capped at 3.83 by B's coverage. The result is not that reasoning scaffolds are unimportant; they improve calibration and discipline. Rather, proprietary evidence sets the upper bound of what the AI Scientist can know and therefore decide.

cs.AI↗
arXiv2026

Ultrafast Sliding Ferroelectric Switching in Bilayer Hexagonal Boron Nitride Revealed by Deep Learning Molecular Dynamics

Sliding ferroelectricity in bilayer hexagonal boron nitride (h-BN) offers compelling prospects for next-generation non-volatile memory, yet the atomistic dynamics of electric-field-driven polarization switching remain poorly understood. Here, we present a fully data-driven, coupled atomistic framework that integrates a fine-tuned MACE machine learning potential (MLP) with an equivariant graph convolutional neural network (EGCNN) for real-time Born effective charge (BEC) prediction, enabling large-scale non-equilibrium molecular dynamics simulations of AB-stacked bilayer h-BN under applied electric fields. By implementing a rigorous real-space path-integral polarization formalism combined with a state-constrained Gaussian convolution background extraction procedure, we successfully isolate the intrinsic spontaneous polarization from the dominant dielectric background. Our simulations reveal that coherent single-domain rigid sliding, completing within 5 ps, constitutes a physically viable ultrafast switching mechanism, and reproduces clean ferroelectric hysteresis loops whose shape is qualitatively consistent with experimental observations.

cond-mat.mtrl-sci↗
arXiv2026

Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning

Accurate crystal structure prediction (CSP) requires accounting for finite-temperature and nuclear quantum effects, yet first-principles evaluation of the free energy surface (FES) remains prohibitive for high-throughput searches. We observe that the self-consistent harmonic approximation (SCHA) FES, as a function of nuclear centroid positions, shares the same mathematical structure as a potential-energy surface and can therefore be directly learned by a deep neural network potential. The resulting deep free energy (DF) model, constructed via a two-level concurrent-learning workflow, evaluates free energies, forces, and stresses in a single forward pass. Applied to the La-Sc-H system at 200 GPa and 300 K, DF-based CSP reproduces the stability of the experimentally observed LaH10 and LaSc2H24, and discovers an unreported thermodynamically stable clathrate hydride: P4/mmm LaScH8. Benchmarked on the LaH10 system, the DF model achieves a 1.72*10^6-fold cost reduction relative to DFT-level SSCHA. The DF framework provides a scalable route for incorporating finite-temperature and nuclear quantum effects into high-throughput crystal structure prediction.

cond-mat.mtrl-sci↗
arXiv2026

Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES

Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine density functional theory (DFT) and machine learning (ML) to interpret how boron (B) and nitrogen (N) dopants influence the local electronic environments of graphene. A dataset of 415 DFT-simulated XANES spectra from 91 distinct configurations was analyzed using a region-specific approach by decomposing each spectrum into pi*, sigma*, and post-edge regions. Random forest models trained on these spectral segments identified the pi* region as the most informative for predicting key local electronic descriptors, particularly the Bader charge and mean dopant-carbon bond length. The Bader charge quantifies dopant-induced charge redistribution and local bonding polarity, directly reflecting the degree of electronic perturbation introduced by heteroatom substitution. The enhanced predictive power of the pi* region arises from its strong coupling to the perturbed pi-electron network, which captures these charge-transfer and hybridization effects more effectively than sigma* or post-edge regions. These findings establish Bader charge as a robust and physically meaningful descriptor for quantifying dopant-induced electronic modulation and demonstrate that region-resolved ML analysis of XANES spectra provides a powerful pathway to uncover structure-property relationships in doped graphene and related materials.

cond-mat.mtrl-sci↗
arXiv2026

Phononic Combs in Lithium Niobate Acoustic Resonators

Frequency combs consist of a spectrum of evenly spaced spectral lines. Optical frequency combs enable technologies ranging from timing, LiDAR, and ultra-stable signal sources. Microwave frequency combs are analogous to optical frequency combs, but often leverage electronic nonlinearity for comb generation. Generating microwave frequency combs using piezoelectric mechanical resonators would enable this behavior in a more compact form factor, thanks to the shorter acoustic wavelengths. In this work, we demonstrate a microwave frequency comb leveraging thermal nonlinearity in high quality factor ($Q$) overmoded acoustic resonators in thin film lithium niobate. By providing input power at 257 MHz, which is the sum frequency of two acoustic modes at 86 MHz and 171 MHz, we generate parametric down conversion and comb generation. We explore the nonlinear mixing regimes and the associated conditions for comb generation. Comb spacing is observed to vary significantly with drive frequency and power, and its general behavior is found to rely heavily on initial conditions. This demonstration showcases the potential for further improvement in compact and efficient microwave frequency combs, leveraging nonlinear acoustic resonators.

physics.app-ph↗
arXiv2026

From Disorder to Function: Entropy-Engineered Broadband Photonics with Ion-Transport-Stabilized Spectral Fidelity

The high-entropy halide-perovskite field has expanded rapidly, yet a key gap remains: configurational entropy is not yet a reliable, designable lever to co-deliver expanded photonic functionality and operational robustness with a composition-transferable mechanistic basis. Here we develop entropy-engineered rare-earth halide double-perovskite single crystals, Cs2Na(Sb, RE)Cl6 (RE3+ = Sc3+, Er3+, Yb3+, Tm3+), that simultaneously expand near-infrared (NIR) functionality and establish a mechanistic stability rule. Near-equiatomic B(III)-site alloying yields a single-phase high-entropy solid solution (Delta_Sconfig about 1.6R). Sb3+ serves as a sensitizer that unifies excitation and cooperatively activates multiple lanthanide channels, transforming the parent single-mode response into a broadband NIR output (~850-1600 nm) with three spectrally orthogonal fingerprint bands at 996, 1220, and 1540 nm. This tri-peak, self-referenced output enables redundancy-based ratiometric solvent identification and quantitative mixture sensing with reduced susceptibility to intensity drift. Accelerated aging under humidity and oxygen shows improved phase and emission stability versus single-component analogues. DFT and molecular dynamics attribute the robustness to strongly suppressed RE$^{3+}$/Cl$^-$ self-diffusion despite comparable H$_2$O/O$_2$ adsorption, kinetically impeding ion-migration-assisted reconstruction and degradation. Integration into a phosphor-converted LED delivers spectrally stable, broadband NIR illumination, establishing entropy engineering as a practical handle to couple expanded photonic functionality with mechanistically accountable durability in metal-halide photonics.

physics.optics↗
arXiv2026

AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios

The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow, demonstrating exceptional performance in coding, deep research, and complex problem-solving evaluations. However, in daily scenarios, the perception of these advanced AI capabilities among general users remains limited. We argue that current evaluations prioritize increasing task difficulty without sufficiently addressing the diversity of agentic tasks necessary to cover the daily work, life, and learning activities of a broad demographic. To address this, we propose AgentIF-OneDay, aimed at determining whether general users can utilize natural language instructions and AI agents to complete a diverse array of daily tasks. These tasks require not only solving problems through dialogue but also understanding various attachment types and delivering tangible file-based results. The benchmark is structured around three user-centric categories: Open Workflow Execution, which assesses adherence to explicit and complex workflows; Latent Instruction, which requires agents to infer implicit instructions from attachments; and Iterative Refinement, which involves modifying or expanding upon ongoing work. We employ instance-level rubrics and a refined evaluation pipeline that aligns LLM-based verification with human judgment, achieving an 80.1% agreement rate using Gemini-3-Pro. AgentIF-OneDay comprises 104 tasks covering 767 scoring points. We benchmarked four leading general AI agents and found that agent products built based on APIs and ChatGPT agents based on agent RL remain in the first tier simultaneously. Leading LLM APIs and open-source models have internalized agentic capabilities, enabling AI application teams to develop cutting-edge Agent products.

cs.CL↗
arXiv2026

62.6 GHz ScAlN Solidly Mounted Acoustic Resonators

We demonstrate a record-high 62.6 GHz solidly mounted acoustic resonator (SMR) incorporating a 67.6 nm scandium aluminum nitride (Sc0.3Al0.7N) piezoelectric layer on a 40 nm buried platinum (Pt) bottom electrode, positioned above an acoustic Bragg reflector composed of alternating SiO2 (28.2 nm) and Ta2O5 (24.3 nm) layers in 8.5 pairs. The Bragg reflector and piezoelectric stack above are designed to confine a third-order thickness-extensional (TE) bulk acoustic wave (BAW) mode, while efficiently transducing with thickness-field excitation. The fabricated SMR exhibits an extracted piezoelectric coupling coefficient (k2) of 0.8% and a maximum Bode quality factor (Q) of 51 at 63 GHz, representing the highest operating frequency reported for an SMR to date. These results establish a pathway toward mmWave SMR devices for filters and resonators in next-generation RF front ends.

eess.SP↗
arXiv2026

Uncooled low-noise thin-film optomechanical resonator for thermal sensing on lithium niobate

Optomechanical transduction harnesses the interaction between optical fields and mechanical motion to achieve sensitive measurement of weak mechanical quantities with inherently low noise. Lithium niobate combines low optical loss, strong piezoelectricity, high intrinsic fQ_m factor, and low thermal conductivity, making it promising for exploring optomechanical platforms targeting thermal sensing applications. Here, we developed an integrated optomechanical platform on thin-film lithium niobate with precisely engineered optical, mechanical, and thermal fields within a compact 40 μm by 40 μm footprint. The platform integrates suspended microring resonators with ultrathin central membranes, reducing mechanical stiffness and effective mass while maintaining a high optical factor Q_o of 1e6 and mechanical quality factor Q_m of 1117, which increases to 5.1e4 after oscillation. The design suppresses thermal dissipation into the silicon substrate and enhances thermal sensitivity, achieving a temperature coefficient of frequency of -124 ppm/K and a noise-equivalent power of 6.2 nW/sqrt(Hz) at 10 kHz at room temperature. This compact and scalable platform opens up new opportunities for high-sensitivity thermal sensing, supports heterogeneous integration with infrared absorbers for uncooled infrared detection, and enables fully integrated, all-optical on-chip readout, paving the way toward large-format, low-noise infrared sensing arrays.

physics.optics↗
arXiv2025

Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application

We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent projection method for calculating atomic magnetic moments by applying a radial truncation to numerical atomic orbitals. A double-loop Lagrange multiplier method is utilized to ensure the satisfaction of constraint conditions while achieving accurate magnetic torque. The method is implemented in ABACUS with both plane wave basis and numerical atomic orbital basis. We benchmark the iron (Fe) systems and analyze differences from calculations with the plane wave basis and numerical atomic orbitals basis in describing magnetic energy barriers. Based on an automated workflow composed of first-principles calculations, magnetic model, active learning, and dynamics simulation, more than 30,000 first-principles data with the information of magnetic torque are generated to train a deep-learning-based magnetic model DeePSPIN for the Fe system. By utilizing the model in large-scale molecular dynamics simulations, we successfully predict Curie temperatures of alpha-Fe close to experimental values.

cond-mat.mtrl-sci↗
arXiv2025

Neural Radiance and Gaze Fields for Visual Attention Modeling in 3D Environments

We introduce Neural Radiance and Gaze Fields (NeRGs), a novel approach for representing visual attention in complex environments. Much like how Neural Radiance Fields (NeRFs) perform novel view synthesis, NeRGs reconstruct gaze patterns from arbitrary viewpoints, implicitly mapping visual attention to 3D surfaces. We achieve this by augmenting a standard NeRF with an additional network that models local egocentric gaze probability density, conditioned on scene geometry and observer position. The output of a NeRG is a rendered view of the scene alongside a pixel-wise salience map representing the conditional probability that a given observer fixates on visible surfaces. Unlike prior methods, our system is lightweight and enables visualization of gaze fields at interactive framerates. Moreover, NeRGs allow the observer perspective to be decoupled from the rendering camera and correctly account for gaze occlusion due to intervening geometry. We demonstrate the effectiveness of NeRGs using head pose from skeleton tracking as a proxy for gaze, employing our proposed gaze probes to aggregate noisy rays into robust probability density targets for supervision.

cs.CV↗
arXiv2025

SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction

Spatiotemporal data mining (STDM) has a wide range of applications in various complex physical systems (CPS), i.e., transportation, manufacturing, healthcare, etc. Among all the proposed methods, the Convolutional Long Short-Term Memory (ConvLSTM) has proved to be generalizable and extendable in different applications and has multiple variants achieving state-of-the-art performance in various STDM applications. However, ConvLSTM and its variants are computationally expensive, which makes them inapplicable in edge devices with limited computational resources. With the emerging need for edge computing in CPS, efficient AI is essential to reduce the computational cost while preserving the model performance. Common methods of efficient AI are developed to reduce redundancy in model capacity (i.e., model pruning, compression, etc.). However, spatiotemporal data mining naturally requires extensive model capacity, as the embedded dependencies in spatiotemporal data are complex and hard to capture, which limits the model redundancy. Instead, there is a fairly high level of data and feature redundancy that introduces an unnecessary computational burden, which has been largely overlooked in existing research. Therefore, we developed a novel framework SparseST, that pioneered in exploiting data sparsity to develop an efficient spatiotemporal model. In addition, we explore and approximate the Pareto front between model performance and computational efficiency by designing a multi-objective composite loss function, which provides a practical guide for practitioners to adjust the model according to computational resource constraints and the performance requirements of downstream tasks.

cs.LG↗
arXiv2025

An 11.7-GHz ScAlN FBAR Filter: Case Study on Scaling Limits and Challenges

This paper reports an 11.7 GHz compact 50 ohm ladder filter based on single layer Scandium Aluminum Nitride (ScAlN) film bulk acoustic resonators (FBARs) with platinum (Pt) electrodes, and uses it as a quantitative case study of the limits encountered when directly scaling to higher frequencies. The measured filter achieves a 3 dB fractional bandwidth (FBW) of 4.0% and an out of band rejection greater than 23.1 dB, with a minimum insertion loss (IL) of 6.8 dB. We analyze the origin of this performance through a quantitative framework: (1) a loss decomposition study, (2) frequency shift sensitivity that explains the discrepancy between simulated and measured center frequency, (3) FBW sensitivity to series shunt separation and port impedance, and (4) stress limited aperture that constrains device size. The results establish a realistic, fabricable baseline for directly scaled single layer ScAlN FBAR filters and outline materials, electrode, and stress management directions toward lower loss mmWave acoustic filters.

physics.app-ph↗