arXiv subjects

Chuhong Wang

Publications and source records attributed to Chuhong Wang.

6 recordsLinked to original sources

arXiv2026

AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries

The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobility, electrochemical stability, and interfacial deformability. Accelerating their discovery requires extensive molecular dynamics, which has been increasingly enabled by universal machine learning interatomic potentials trained on foundational datasets. However, the dynamic softness of halides poses a stringent test of whether general-purpose models can reliably replace first-principles calculations under the highly distorted, elevated-temperature regimes necessary to probe ion transport. Here, we present AQVolt26, a dataset of 322,656 r$^2$SCAN single-point calculations for lithium halides, generated via high-temperature configurational sampling across $\sim$5K structures. We demonstrate that foundational datasets provide a strong baseline for stable halide chemistries and transfer local forces well, however absolute energy predictions degrade in distorted higher-temperature regimes. Co-training with AQVolt26 resolves this blind spot. Furthermore, incorporating Materials Project relaxation data improves near-equilibrium performance but degrades extreme-strain robustness without enhancing high-temperature force accuracy. These results demonstrate that domain-specific configurational sampling is essential for the reliable dynamic screening of halide electrolytes. Furthermore, our findings suggest that while foundational models provide a robust base, they are most effective for dynamically soft solid-state chemistries when augmented with targeted, high-temperature data. Finally, we show that near-equilibrium relaxation data serves as a task-specific complement rather than a universally beneficial addition.

cond-mat.mtrl-sci↗

arXiv2025

AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a complementary dataset of 13.5 million density functional theory (DFT) single point calculations designed to improve the treatment of systems where spin polarization and/or higher fidelity are critical. We also investigate methodologies for integrating new datasets, such as AQCat25, with the broader Open Catalyst 2020 (OC20) dataset to create spin-aware models without sacrificing generalizability. We find that directly tuning a general model on AQCat25 leads to catastrophic forgetting of the original dataset's knowledge. Conversely, joint training strategies prove effective for improving accuracy on the new data without sacrificing general performance. This joint approach introduces a challenge, as the model must learn from a dataset containing both mixed-fidelity calculations and mixed-physics (spin-polarized vs. unpolarized). We show that explicitly conditioning the model on this system-specific metadata, for example by using Feature-wise Linear Modulation (FiLM), successfully addresses this challenge and further enhances model accuracy. Ultimately, our work establishes an effective protocol for bridging DFT fidelity domains to advance the predictive power of foundational models in catalysis.

cond-mat.mtrl-sci↗

arXiv2025

Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset

With the development of underwater exploration and marine protection, underwater vision tasks are widespread. Due to the degraded underwater environment, characterized by color distortion, low contrast, and blurring, camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings. Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes. To address these issues, we introduce the first underwater camouflaged instance segmentation (UCIS) dataset, abbreviated as UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations. In addition, we propose an Underwater Camouflaged Instance Segmentation network based on Segment Anything Model (UCIS-SAM). Our UCIS-SAM includes three key modules. First, the Channel Balance Optimization Module (CBOM) enhances channel characteristics to improve underwater feature learning, effectively addressing the model's limited understanding of underwater environments. Second, the Frequency Domain True Integration Module (FDTIM) is proposed to emphasize intrinsic object features and reduce interference from camouflage patterns, enhancing the segmentation performance of camouflaged objects blending with their surroundings. Finally, the Multi-scale Feature Frequency Aggregation Module (MFFAM) is designed to strengthen the boundaries of low-contrast camouflaged instances across multiple frequency bands, improving the model's ability to achieve more precise segmentation of camouflaged objects. Extensive experiments on the proposed UCIS4K and public benchmarks show that our UCIS-SAM outperforms state-of-the-art approaches.

cs.CV↗

arXiv2025

Beyond Global Scanning: Adaptive Visual State Space Modeling for Salient Object Detection in Optical Remote Sensing Images

Salient object detection (SOD) in optical remote sensing images (ORSIs) faces numerous challenges, including significant variations in target scales and low contrast between targets and the background. Existing methods based on vision transformers (ViTs) and convolutional neural networks (CNNs) architectures aim to leverage both global and local features, but the difficulty in effectively integrating these heterogeneous features limits their overall performance. To overcome these limitations, we propose an adaptive state space context network (ASCNet), which builds upon the state space model mechanism to simultaneously capture long-range dependencies and enhance regional feature representation. Specifically, we employ the visual state space encoder to extract multi-scale features. To further achieve deep guidance and enhancement of these features, we design a Multi-Level Context Module (MLCM), which module strengthens cross-layer interaction capabilities between features of different scales while enhancing the model's structural perception, allowing it to distinguish between foreground and background more effectively. Then, we design the Adaptive Patchwise Visual State Space (APVSS) block as the decoder of ASCNet, which integrates our proposed Dynamic Adaptive Granularity Scan (DAGS) and Granularity-aware Propagation Module (GPM). It performs adaptive patch scanning on feature maps enhanced by local perception, thereby capturing rich local region information and enhancing state space model's local modeling capability. Extensive experimental results demonstrate that the proposed model achieves state-of-the-art performance, validating its effectiveness and superiority.

cs.CV↗

arXiv2022

Reactive Laser Synthesis of Ultra-high-temperature Ceramics HfC, ZrC, TiC, HfN, ZrN, and TiN for Additive Manufacturing

Ultra-high-temperature ceramics (UHTCs) are optimal structural materials for applications that require extreme temperature resilience, resistance to chemically aggressive environments, wear, and mechanical stress. Processing UHTCs with laser-based additive manufacturing (AM) has not been fully realized due to a variety of obstacles. In this work, selective laser reaction sintering (SLRS) techniques were investigated for the production of near net-shape UHTC ceramics such as HfC, ZrC, TiC, HfN, ZrN, and TiN. Group IV transition metal and metal oxide precursor materials were chemically converted and reaction-bonded into layers of UHTCs using single-step selective laser processing in CH4 or NH3 gas that might be compatible with prevailing powder bed fusion techniques. Conversion of either metals (Hf, Zr and Ti) or metal oxides (HfO2, ZrO2, and TiO2) particles was first investigated to examine reaction mechanisms and volume changes associated with SLRS of single-component precursor systems. SLRS processing of metal or metal oxide alone produced near stoichiometric UHTC phases with yields up to 100 wt% total for carbides and nitrides. However, for single component precursors, gas-solid reactivity induced volumetric changes resulted in residual stresses and cracking in the product layer. To mitigate conversion-induced stresses, composite metal/metal oxide precursors were employed to compensate for the volume changes of either the metal (which expands during conversion) or the metal oxide precursor (which contracts).

cond-mat.mtrl-sci↗

arXiv2022

Selective Laser Reaction Synthesis of SiC, Si$_3$N$_4$ and HfC/SiC Composites for Additive Manufacturing

Selective laser reaction sintering techniques (SLRS) techniques were investigated for the production of near net-shape non-oxide ceramics including SiC, Si$_3$N$_4$, and HfC/SiC composites that might be compatible with prevailing powder bed fusion additive manufacturing processes. Reaction bonded layers of covalent ceramics were produced using in-situ reactions that occur during selective laser processing and layer formation. During SLRS, precursor materials composed of metal and/or metal oxide powders were fashioned into powder beds for conversion to non-oxide ceramic layers. Laser-processing was used to initiate simultaneous chemical conversion and local interparticle bonding of precursor particles in CH4 or NH3 gases. Several factors related to the reaction synthesis process (precursor chemistry, gas-solid and gas-liquid synthesis mechanisms, precursor vapor pressures) were investigated in relation to resulting microstructures and non-oxide yields. Results indicated that the volumetric changes which occurred during in-situ conversion of single component precursors negatively impacted the surface layer microstructure. To circumvent the internal stresses and cracking that accompanied the conversion of Si or Hf (that expands upon conversion) or SiO$_x$ (that contracts during conversion), optimized ratios of the precursor constituents were used to produce near isovolumetric conversion to the product phase. The results demonstrate that under appropriate processing conditions and precursor selection, the formation of near net-shape SiC and SiC composites might be achieved through single-step AM-compatible techniques.

cond-mat.mtrl-sci↗