arXiv subjects

Wenbo Liu

Publications and source records attributed to Wenbo Liu.

At least 19 recordsLinked to original sources

arXiv2026

RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction

Retrosynthesis is a cornerstone of drug discovery and organic synthesis. While data-driven deep learning models have shown remarkable progress, they autonomously learn reaction patterns from extensive datasets with limited integration of established chemical knowledge as priors. To address this limitation, we introduce RetroMPA, a molecular property-aware, post-hoc enhancement module that injects chemical knowledge into the retrosynthesis pipeline. Rather than functioning as an independent SMILES sequence generator, RetroMPA is a broadly applicable, model-agnostic chemical filter designed to recalibrate and optimize the predictive pathways of existing algorithms. This plug-and-play framework integrates seamlessly with a range of data-driven retrosynthesis methods, enhancing outputs without modifying model architecture or requiring resource-intensive retraining. By leveraging a property-aware latent embedding space, RetroMPA consistently improves top-1 accuracy across eight representative retrosynthesis models by an average of 5.50% on USPTO-50K. Furthermore, we validate its scalability on the large-scale USPTO-Full dataset, achieving an average improvement of about 2.03% across both template-based and template-free architectures. Wet-lab experiments provide preliminary support for the practical utility of the framework. These syntheses confirmed viable, previously unreported substrate combinations for classic reaction paradigms---specifically, Suzuki-Miyaura coupling, Bucherer reaction, and Friedel-Crafts acylation---suggesting that RetroMPA can operate beyond mere data fitting. The code is open-sourced at https://github.com/MengzhouLu/RetroMPA.

cs.LG↗

arXiv2026

Unveiling Nanoscale Surface Damage Dynamics in Swift Heavy Ion Irradiated Gallium Nitride

This work systematically unveils the nanoscale surface damage dynamics in gallium nitride by investigating the atomistic mechanisms of hillock formation. The results identify two distinct hillock morphologies dependent on electronic energy loss (Se) values. Bell-shaped hillocks form under 18.2 keV/nm Kr irradiation, whereas crater-rim hillocks with central holes emerge under 40.2 keV/nm Ta irradiation. Microstructural analysis reveals that Ga-rich hillocks are accompanied by the generation of metastable zincblende nanodomains. These nanodomains preferentially aggregate around the periphery or sidewalls of the hillocks and exhibit a high spatial correlation with screw dislocations. Further temperature-dependent studies indicate that elevated temperatures significantly enlarge the overall dimensions of the hillock structures without altering their fundamental morphologies. Notably, under Ta irradiation above 1200 K, the high temperatures drastically reduce the viscosity and surface tension of liquid gallium. This enhanced fluidity of the transient molten phase promotes the formation of penetrating nanochannels.

cond-mat.mtrl-sci↗

arXiv2026

Sub-Terahertz Channel Performance under Snowfall

The terahertz (THz) band promises terabit-per-second links but is highly sensitive to snowfall. Natural snowflakes are non-spherical. Yet existing THz studies treat them as spheres under Mie theory, and no ITU-R model covers THz snow attenuation. This work combines line-of-sight measurements at 120, 140, and 160 GHz with physics-based scattering modeling. The measured loss is compared against the ITU-R P.1817-1 optical model, Mie models, and a discrete dipole approximation (DDA) for randomly oriented hexagonal-plate ice crystals, each with the Scott and Gunn-Marshall size distributions. Over the measured band, ITU-R P.1817-1 overestimates and the Mie models underestimate the loss. The shape-aware DDA-Scott model agrees best, with the lowest RMSE at every frequency. From DDA-Scott, we derive a compact modified ITU-R expression in carrier frequency and liquid-water-equivalent (LWE) rate. It reproduces the reference to within 2.5 dB/km over 100-500 GHz and 0-3 mm/h. A Rician K-factor analysis shows the channel stays LoS-dominated, so snowfall degrades the link mainly through attenuation, not multipath fading. A QPSK/16-QAM link-budget analysis then quantifies the cost of the spherical assumption. Mie-based margins overestimate the tolerable snowfall rate by 3.4 across 120-160 GHz, rising toward 5.8 in the upper transparency windows by model extrapolation. The model is further mapped into snow-limited range and adaptive-modulation switching boundaries. These results support future ITU-R recommendations for THz channels under snowfall.

physics.app-ph↗

arXiv2026

Directed extended-range percolation

While for standard percolation directionality is known to increase the combinatorial complexity of percolation, here we show that when connectivity is ensured by paths of length $R\geq 2$, network directionality, impeding backtracking, can significantly reduce the complexity of percolation. To illustrate this finding, we introduce Directed Extended-Range Percolation (DERP), defined directed networks with non-reciprocal edges, motivated by applications in quantum communication. In this framework, message transmission is enabled between trusted nodes separated by a directed path of length at most $R$. Using a message-passing approach, we show that directionality enables an exact determination of the percolation threshold and the anomalous critical indices on locally tree-like structures. On random directed networks we find that the critical behavior of DERP depends sensitively on degree correlations. These analytical predictions are corroborated by extensive Monte Carlo simulations, highlighting the profound impact of directionality and correlations on long-range connectivity in complex networks.

cond-mat.dis-nn↗

arXiv2026

Smoothing Binary Optimization: A Primal-Dual Perspective

Binary optimization is a powerful tool for modeling combinatorial problems, yet scalable and theoretically sound solution methods remain elusive. Conventional solvers often rely on heuristic strategies with weak guarantees or struggle with large-scale instances. In this work, we introduce a novel primal-dual framework that reformulates unconstrained binary optimization as a continuous minimax problem, satisfying a strong max-min property. This reformulation effectively smooths the discrete problem, enabling the application of efficient gradient-based methods. We propose a simultaneous gradient descent-ascent algorithm that is highly parallelizable on GPUs and provably converges to a near-optimal solution in linear time. Extensive experiments on large-scale problems--including Max-Cut, MaxSAT, and Maximum Independent Set with up to 50,000 variables--demonstrate that our method identifies high-quality solutions within seconds, significantly outperforming state-of-the-art alternatives.

math.OC↗

arXiv2026

Near-Field Coupling of Polypropylene Dielectric Waveguide Routed Near PCB Board at Terahertz Frequencies

The growing demand for high-capacity, low-loss short-reach links in highly integrated electronic systems makes it necessary to understand how terahertz (THz) dielectric waveguides behave in realistic PCB-level packaging environments. In this article, we investigate the channel transmission of a 3D-printed polypropylene dielectric waveguide placed near representative PCB substrates. Continuous-wave THz measurements are carried out for bare, fully copper-clad, and periodic copper-trTerahertz (THz) dielectric waveguides are promising physical channels for short-reach interconnects, but their air-clad guided fields may interact with nearby printed-circuit-board (PCB) structures in compact packages. In this work, we experimentally and numerically investigate PCB-proximity-induced excess transmission loss in 3D-printed polypropylene rectangular dielectric waveguides over 220-325 GHz. Continuous-wave transmission measurements are performed for bare FR4, continuous waveguide-facing copper, and periodic copper-trace PCB configurations under controlled clearance and alignment conditions. The results show that direct contact with bare FR4 can induce a frequency-selective high-loss band, which is attributed to phase-matched leakage from the guided waveguide mode into a substrate-supported leaky branch. This finding highlights PCB proximity as a critical layout factor and provides practical guidance for clearance control and metallization design in compact THz dielectric-waveguide packages.ace PCBs with different waveguide-PCB separations, while terahertz time-domain spectroscopy is used to characterize the dielectric properties of the substrate materials.

physics.app-ph↗

arXiv2026

AI assisted optimization of integrated waveguide polarizers containing 2D reduced graphene oxide

Reduced graphene oxide (rGO) exhibits strong anisotropic light absorption and high compatibility with photonic integrated chips, making it a promising material for implementing high performance onchip polarization selective devices. The performance of rGO integrated waveguide polarizers is highly dependent on the waveguide geometry, and achieving optimal performance requires exploring a large parameter space, making conventional mode simulation methods computationally demanding. Here, we propose and demonstrate a machine learning framework based on fully connected neural networks (FCNNs) to map the dependence of the polarizer figure of merit (FOM) on the waveguide geometry. Once trained by using a small dataset of low resolution mode simulation results, the FCNN framework can rapidly and accurately predict FOM values across a large structural parameter space with high resolution. Results show that this method can reduce overall computing time by more than 4 orders of magnitude as compared to the mode simulation methods, and achieve high prediction accuracy with an average deviation (AD) below 0.05. These results highlight the FCNN based machine learning framework as an efficient tool for the design and optimization of rGO integrated waveguide polarizers.

physics.optics↗

arXiv2026

AI based design of 2D material integrated optical polarizers

On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high performance polarization selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning (ML) model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material based optical polarizers with high efficiency.

physics.optics↗

arXiv2026

Parallel Graver Basis Extraction for Nonlinear Integer Optimization

The augmentation scheme provides a nontraditional approach to nonlinear integer programming by iteratively refining incumbent solutions along objective-improving directions from the Graver basis. Its main computational bottleneck, however, lies in the practical difficulty of accessing such directions. To address this challenge, we develop a massively parallel heuristic for approximating Graver basis, extracting promising directions by optimizing nonconvex continuous problems using parallelizable first-order methods. Experiments on QPLIB and MINLPLib instances show that our method achieves comparable performance to advanced solvers.

math.OC↗

arXiv2026

Measurement and Modeling of Structure-Induced Surface Scattering on Terahertz Channel

As terahertz (THz) frequencies emerge as promising candidates for next-generation wireless networks, accurate characterization of propagation mechanisms in indoor/outdoor environments becomes essential for system design and performance optimization. This article presents an experimental and theoretical investigation of structure-induced indoor surface scattering on THz channels, examining how material properties and structural configurations jointly govern channel power and angular distribution. Six representative indoor surfaces are characterized, revealing that intrinsic structural inhomogeneity -- particularly the quasi-periodic earlywood-latewood arrangement in pine wood -- induces measurable angular scattering whose dominant lobes and angular shifts are reproduced by a beam-propagation modeling (BPM) framework. Material-covered surface configurations are further investigated, demonstrating that thin dielectric covering layers can substantially modify reflection characteristics through thickness- and frequency- dependent thin-film interference effects. Wide-angle bistatic measurements conducted in a conference-room environment reveal that structured indoor elements, such as folded curtains, can enhance angular scattering and extend spatial coverage. These findings establish that structure-induced surface scattering mechanisms offer potential for constructing non-line-of-sight THz links in indoor environments.

physics.app-ph↗

arXiv2026

Synthesis of Monolayer Ice on a Hydrophobic Metal Surface

Understanding water-metal interactions is central to disciplines spanning catalysis, electrochemistry, and atmospheric science. Monolayer ice phases are well established on hydrophilic surfaces, where strong water-substrate interactions stabilize ordered hydrogen-bond networks. In contrast, their formation on hydrophobic metals has been deemed ther-modynamically unfavourable, with water typically assembling into amorphous films, three-dimensional crystallites, or interlocked bilayer ice. Here, we demonstrate the synthesis of a monolayer ice phase on the hydrophobic Au(111) surface using a low-energy-electron-assisted growth method. Combined experimental characterizations including low-energy electron diffraction, angle-resolved photoemission spectroscopy, and X-ray photoelectron spectroscopy, complemented by first-principles calculations, prove that the monolayer ice phase composes of intact water molecules. This approach provides a generalizable strategy for stabilizing ordered two-dimensional ice on inert substrates and offers new insight into the interplay between water and low-energy electrons at hydrophobic interfaces.

cond-mat.mtrl-sci↗

arXiv2025

Development of a 10.8-eV Tabletop Femtosecond Laser with Tunable Polarization for High-Resolution Angle-Resolved Photoemission Spectroscopy

The development of extreme ultraviolet sources is critical for advancing angleresolved photoemission spectroscopy (ARPES), a powerful technique for probing the electronic structure of materials. Here, we report the construction of a tabletop 10.8-eV femtosecond laser through cascaded third-harmonic generation, which operates at a repetition rate of 1 MHz and delivers a photon flux of approximately 1012 photons/s. The system achieves a high energy resolution of approximately 11.8 meV and tunable polarization. This flexibility enables detailed studies of orbitaland (pseudo)spin characteristics in quantum materials. We demonstrate the capabilities of this laser-ARPES system by investigating several prototypical materials, showcasing its potential for elucidating complex phenomena in quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

Fully automatic fabrication of fibre Bragg gratings using an AI-powered femtosecond laser inscription system

Fibre Bragg gratings (FBGs) are widely used in optical sensing and communication systems. Femtosecond laser inscription (FLI) enables hydrogen-free, thermally stable, high-resolution, and complex structures of FBG fabrication, but its practical application is limited by manual operation, low throughput, and sensitivity to laser alignment. In this study, we present an AI-powered FLI system that enables automated, stable, and efficient FBG fabrication. By integrating a Multi-Layer Perceptron (MLP) model for real-time fabrication position correction, the system maintains precise laser alignment (-0.6 to 0.2 microns of the fibre core plane) and ensures consistent processing. Strong and weak FBGs were fabricated in different types of fibres, and their spectral characteristics-including central wavelength, reflectivity, and FWHM-exhibited high stability and repeatability. The results demonstrate that the proposed AI-powered FLI system significantly reduces manual intervention while achieving reliable FBG performance. This approach holds great promise for scalable, high-throughput FBG production and can be extended to the fabrication of arbitrary FBG structures across various fibre types. With further training and model refinement, the AI-powered FLI provides a scalable and intelligent platform for next-generation automated FBG manufacturing.

physics.optics↗

arXiv2025

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions

The terahertz (THz) band is a promising candidate for sixth-generation wireless networks, but its deploymen in outdoor environments is challenged by meteorological phenomena, particularly fog, which imposes variable and difficult-to-predict channel degradation. This article introduces dynamic channel model for the THz band explicitly driven by the time-evolving droplet size distribution (DSD) of fog, integrating real-time microphysical sensing to capture variations in the fog microstructure. Experimental measurements were conducted at 220 GHz and 320 GHz in a controlled fog chamber to achieve quasi-stationary states, and a larger room-scale setup to characterize dynamic, non-stationary fog evolution. The results confirm that channel power loss is overwhelmingly dominated by absorption rather than scattering, validating the use of the computationally efficient Rayleigh approximation below 1 THz. Statistical analysis revealed exceptionally high Rician K-factors, demonstrating that THz channels maintain strong line-of-sight stability even in dense fog. System-level performance analysis shows that degradation in bit error rate is driven by the slow, gradual evolution of the DSD, rather than fast multipath fading. This finding enables the reliable simplification of the THz fog channel into a near-Gaussian channel model with time-varying signal-to-noise ratio. This microphysics-aware approach established here provides the necessary foundation for developing adaptive system designs centered on SNR tracking for robust future THz networks.

physics.app-ph↗

arXiv2025

Propagation Performance of Terahertz Channels in Lunar Dust

The growing lunar exploration programs require robust communication systems for dust-laden environments, necessitating comprehensive understanding of channel propagation characteristics. We present an analysis of terahertz channel propagation through lunar dust environments, critical for reliable communication and sensing infrastructure. We develop an extended Mie scattering model incorporating unique properties of lunar dust particles from Apollo samples (10084, 14003, 70051), including irregular morphology, dielectric characteristics, and charge-dependent behavior. Through theoretical analysis and experimental verification, we examine power and bit error rate performance across varying dust conditions, revealing distinct relationships between particle characteristics and channel performance.

physics.app-ph↗

arXiv2025

Eavesdropping Risk in Terahertz Channels by Covered Wavy Surfaces

Terahertz communications offer unprecedented data rates for next-generation wireless networks but suffer blockage susceptibility that restrict coverage and introduce physical-layer security vulnerabilities. Non-line-of-sight relay schemes using metallic wavy surfaces (MWS) address coverage limitations but require concealment beneath indoor materials for practical deployment. This work investigates THz channel characteristics and security vulnerabilities when MWS surfaces are covered with wallpaper, curtain, and wall plaster across 113-170 GHz. Results reveal that covering materials redistribute rather than eliminate eavesdropping threats, with persistent feasible interception scenarios remaining undetectable through conventional backscattering monitoring. These findings underscore the need for enhanced mechanisms designed for covered reflecting elements.

physics.app-ph↗

arXiv2025

Terahertz channel performance under dynamic water surface reflections

As the terahertz (THz) band emerges as a pivotal technology for next-generation wireless communications, accurate channel modeling in dynamic environments becomes increasingly critical, particularly for scenarios involving reflective interactions with water surfaces. This article presents comprehensive experimental and theoretical investigations into THz channel (120-320 GHz) performance under dynamic water surface reflections. By developing and validating a modified dual-scale scattering model based on the improved integral equation model (I2EM), this work systematically evaluates channel characteristics, such as signal power loss and bit error rate (BER), across various dynamic aquatic scenarios. Laboratory experiments and real-world natatorium measurements demonstrate the model's efficacy in capturing complex temporal and spatial scattering behaviors, offering vital insights and robust predictive capabilities essential for deploying possible THz communication systems in aquatic and sports environments.

physics.app-ph↗

arXiv2025

A Multi-Agent Generative AI Framework for IC Module-Level Verification Automation

As large language models demonstrate enormous potential in the field of Electronic Design Automation (EDA), generative AI-assisted chip design is attracting widespread attention from academia and industry. Although these technologies have made preliminary progress in tasks such as code generation, their application in chip verification -- a critical bottleneck in the chip development cycle -- remains at an exploratory stage. This paper proposes an innovative Multi-Agent Verification Framework (MAVF) aimed at addressing the limitations of current single-LLM approaches in complex verification tasks. Our framework builds an automated transformation system from design specifications to testbench through the collaborative work of multiple specialized agents, including specification parsing, verification strategy generation, and code implementation. Through verification experiments on multiple chip modules of varying complexity, results show that MAVF significantly outperforms traditional manual methods and single-dialogue generative AI approaches in verification document parsing and generation, as well as automated testbench generation. This research opens new directions for exploring generative AI applications in verification automation, potentially providing effective approaches to solving the most challenging bottleneck issues in chip design.

cs.AR↗