arXiv subjects

Qimin Wang

Publications and source records attributed to Qimin Wang.

3 recordsLinked to original sources

arXiv2026

Interface-Controlled Defect Engineering in TiN/TaN Superlattices for Enhanced Hardness and Fracture Toughness

TiNTaN superlattice coatings were designed to investigate how atomic-scale interface chemistry and defect-stabilized TaN layers govern hardness and fracture toughness. Guided by first-principles predictions identifying TaN-based layers as more damage tolerant than TiN, coherent superlattices with a bilayer period of 6 nm were synthesized by reactive magnetron sputtering and interfacially doped with C, B, or Si. Structural and chemical analyses reveal coherent fcc architectures with well-defined interfaces. Si segregates preferentially to the interfaces while incorporating into both TiN and TaN, whereas C and B predominantly diffuse into the TaN layers, modifying coherency strain, bonding, and defect populations. Consequently, hardness increases from 34 GPa for the undoped superlattice to 41 GPa for the Si-doped architecture, whereas fracture toughness increases from 2.8 to 4.0 MPam0.5 for the B-doped superlattice. First-principles calculations show that vacancy-stabilized TaxNy enhances elastic compliance and elastic contrast rather than intrinsic toughness, while the additional toughening induced by B indicates localized defect-assisted energy dissipation at chemically engineered interfaces. Thus, Si maximizes interface strengthening, whereas B provides the most favourable hardness-toughness balance while preserving high hardness, 38 GPa. These findings establish interface chemistry as an additional design parameter for tailoring the mechanical performance of ceramic nitride superlattices.

cond-mat.mtrl-sci↗

arXiv2026

Artificial intelligence for partial differential equations in computational mechanics: A review

In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve partial differential equations (AI for PDEs: Artificial intelligence for partial differential equations) has become a focal point in computational mechanics. The core of AI for PDEs is the fusion of data and partial differential equations (PDEs), which can solve almost any PDEs. Therefore, this article provides a comprehensive review of the research on AI for PDEs, summarizing the existing algorithms and theories. The article discusses the applications of AI for PDEs in computational mechanics, including solid mechanics, fluid mechanics, and biomechanics. The existing AI for PDEs algorithms include those based on Physics-Informed Neural Networks (PINNs), Deep Energy Methods (DEM), Operator Learning, and Physics-Informed Neural Operator (PINO). AI for PDEs represents a new method of scientific simulation that provides approximate solutions to specific problems using large amounts of data, then fine-tuning according to specific physical laws, avoiding the need to compute from scratch like traditional algorithms. Thus, AI for PDEs is the prototype for future foundation models in computational mechanics, capable of significantly accelerating traditional numerical algorithms.

eess.SY↗

arXiv2025

GuidPaint: Class-Guided Image Inpainting with Diffusion Models

In recent years, diffusion models have been widely adopted for image inpainting tasks due to their powerful generative capabilities, achieving impressive results. Existing multimodal inpainting methods based on diffusion models often require architectural modifications and retraining, resulting in high computational cost. In contrast, context-aware diffusion inpainting methods leverage the model's inherent priors to adjust intermediate denoising steps, enabling high-quality inpainting without additional training and significantly reducing computation. However, these methods lack fine-grained control over the masked regions, often leading to semantically inconsistent or visually implausible content. To address this issue, we propose GuidPaint, a training-free, class-guided image inpainting framework. By incorporating classifier guidance into the denoising process, GuidPaint enables precise control over intermediate generations within the masked areas, ensuring both semantic consistency and visual realism. Furthermore, it integrates stochastic and deterministic sampling, allowing users to select preferred intermediate results and deterministically refine them. Experimental results demonstrate that GuidPaint achieves clear improvements over existing context-aware inpainting methods in both qualitative and quantitative evaluations.

cs.CV↗