arXiv subjects

Feng Ding

Publications and source records attributed to Feng Ding.

At least 55 records · Page 3Linked to original sources

arXiv2025

Advances in modeling complex materials: The rise of neuroevolution potentials

Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding the physical and chemical properties of materials. In recent years, machine-learned potentials (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide a desirable balance between accuracy and computational cost. The neuroevolution potential (NEP) approach, implemented in the open-source GPUMD software, has emerged as a promising machine-learned potential, exhibiting impressive accuracy and exceptional computational efficiency. This review provides a comprehensive discussion on the methodological and practical aspects of the NEP approach, along with a detailed comparison with other representative state-of-the-art MLP approaches in terms of training accuracy, property prediction, and computational efficiency. We also demonstrate the application of the NEP approach to perform accurate and efficient MD simulations, addressing complex challenges that traditional force fields typically can not tackle. Key examples include structural properties of liquid and amorphous materials, chemical order in complex alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture in two-dimensional materials, nanoscale tribology, and mechanical behavior of compositionally complex alloys under various mechanical loadings. This review concludes with a summary and perspectives on future extensions to further advance this rapidly evolving field.

cond-mat.mtrl-sci↗

arXiv2024

Theoretical Studies on Sodium Storage Mechanism in Hard Carbon Anodes of Sodium-Ion Batteries: Molecular Simulations Based on Machine Learning Force Fields

Sodium-ion batteries (SIBs) have garnered significant attention in recent years as a promising alternative to lithium-ion batteries (LIBs) due to their low cost, abundant sodium resources, and excellent cycling performance. Hard carbon materials, characterized by their high specific capacity, outstanding cycling stability, and low cost, have emerged as potential candidates for SIB anodes. However, the sodium storage mechanism in hard carbon anodes remains highly complex, especially in disordered structures, and is yet to be fully understood. To address this, we employed relative machine learning force fields (MLFFs) in conjunction with multiscale simulation techniques to systematically investigate the sodium storage behavior in hard carbon. By integrating simulations, this study provides a detailed exploration of sodium adsorption, intercalation, and filling mechanisms. High-precision, large-scale simulations reveal the dynamic behavior and distribution patterns of sodium ions in hard carbon. The findings not only deepen our understanding of sodium storage mechanisms in hard carbon anodes, but also offer a theoretical foundation for optimizing future SIB designs, while introducing novel simulation methodologies and technical frameworks to enhance battery performance.

cond-mat.mtrl-sci↗

arXiv2024

Stochastic Search Variable Selection for Bayesian Generalized Linear Mixed Effect Models

Variable selection remains a difficult problem, especially for generalized linear mixed models (GLMMs). While some frequentist approaches to simultaneously select joint fixed and random effects exist, primarily through the use of penalization, existing approaches for Bayesian GLMMs exist only for special cases, like that of logistic regression. In this work, we apply the Stochastic Search Variable Selection (SSVS) approach for the joint selection of fixed and random effects proposed in Yang et al. (2020) for linear mixed models to Bayesian GLMMs. We show that while computational issues remain, SSVS serves as a feasible and effective approach to jointly select fixed and random effects. We demonstrate the effectiveness of the proposed methodology to both simulated and real data. Furthermore, we study the role hyperparameters play in the model selection.

stat.ME↗

arXiv2024

Decoupling Forgery Semantics for Generalizable Deepfake Detection

In this paper, we propose a novel method for detecting DeepFakes, enhancing the generalization of detection through semantic decoupling. There are now multiple DeepFake forgery technologies that not only possess unique forgery semantics but may also share common forgery semantics. The unique forgery semantics and irrelevant content semantics may promote over-fitting and hamper generalization for DeepFake detectors. For our proposed method, after decoupling, the common forgery semantics could be extracted from DeepFakes, and subsequently be employed for developing the generalizability of DeepFake detectors. Also, to pursue additional generalizability, we designed an adaptive high-pass module and a two-stage training strategy to improve the independence of decoupled semantics. Evaluation on FF++, Celeb-DF, DFD, and DFDC datasets showcases our method's excellent detection and generalization performance. Code is available at: https://github.com/leaffeall/DFS-GDD.

cs.CV↗

arXiv2024

Crack Detection in Infrastructure Using Transfer Learning, Spatial Attention, and Genetic Algorithm Optimization

Crack detection plays a pivotal role in the maintenance and safety of infrastructure, including roads, bridges, and buildings, as timely identification of structural damage can prevent accidents and reduce costly repairs. Traditionally, manual inspection has been the norm, but it is labor-intensive, subjective, and hazardous. This paper introduces an advanced approach for crack detection in infrastructure using deep learning, leveraging transfer learning, spatial attention mechanisms, and genetic algorithm(GA) optimization. To address the challenge of the inaccessability of large amount of data, we employ ResNet50 as a pre-trained model, utilizing its strong feature extraction capabilities while reducing the need for extensive training datasets. We enhance the model with a spatial attention layer as well as a customized neural network which architecture was fine-tuned using GA. A comprehensive case study demonstrates the effectiveness of the proposed Attention-ResNet50-GA model, achieving a precision of 0.9967 and an F1 score of 0.9983, outperforming conventional methods. The results highlight the model's ability to accurately detect cracks in various conditions, making it highly suitable for real-world applications where large annotated datasets are scarce.

cs.CV↗

arXiv2024

FairAdapter: Detecting AI-generated Images with Improved Fairness

The high-quality, realistic images generated by generative models pose significant challenges for exposing them.So far, data-driven deep neural networks have been justified as the most efficient forensics tools for the challenges. However, they may be over-fitted to certain semantics, resulting in considerable inconsistency in detection performance across different contents of generated samples. It could be regarded as an issue of detection fairness. In this paper, we propose a novel framework named Fairadapter to tackle the issue. In comparison with existing state-of-the-art methods, our model achieves improved fairness performance. Our project: https://github.com/AppleDogDog/FairnessDetection

cs.CV↗

arXiv2024

Channel-Adaptive Wireless Image Semantic Transmission with Learnable Prompts

Recent developments in Deep learning based Joint Source-Channel Coding (DeepJSCC) have demonstrated impressive capabilities within wireless semantic communications system. However, existing DeepJSCC methodologies exhibit limited generalization ability across varying channel conditions, necessitating the preparation of multiple models. Optimal performance is only attained when the channel status during testing aligns precisely with the training channel status, which is very inconvenient for real-life applications. In this paper, we introduce a novel DeepJSCC framework, termed Prompt JSCC (PJSCC), which incorporates a learnable prompt to implicitly integrate the physical channel state into the transmission system. Specifically, the Channel State Prompt (CSP) module is devised to generate prompts based on diverse SNR and channel distribution models. Through the interaction of latent image features with channel features derived from the CSP module, the DeepJSCC process dynamically adapts to varying channel conditions without necessitating retraining. Comparative analyses against leading DeepJSCC methodologies and traditional separate coding approaches reveal that the proposed PJSCC achieves optimal image reconstruction performance across different SNR settings and various channel models, as assessed by Peak Signal-to-Noise Ratio (PSNR) and Learning-based Perceptual Image Patch Similarity (LPIPS) metrics. Furthermore, in real-world scenarios, PJSCC shows excellent memory efficiency and scalability, rendering it readily deployable on resource-constrained platforms to facilitate semantic communications.

eess.IV↗

arXiv2024

Experimental Catalytic Amplification of Asymmetry

The manipulation and transformation of quantum resources are key parts of quantum mechanics. Among them, asymmetry is one of the most useful operational resources, which is widely used in quantum clocks, quantum metrology, and other tasks. Recent studies have shown that the asymmetry of quantum states can be significantly amplified with the assistance of correlating catalysts which are finite-dimensional auxiliaries. In the experiment, we perform translationally invariant operations, ensuring that the asymmetric resources of the entire system remain non-increasing, on a composite system composed of a catalytic system and a quantum system. The experimental results demonstrate an asymmetry amplification of 0.0172\pm0.0022 in the system following the catalytic process. Our work showcases the potential of quantum catalytic processes and is expected to inspire further research in the field of quantum resource theories.

quant-ph↗

arXiv2024

Novel Atmospheric Dynamics Shape Inner Edge of Habitable Zone Around White Dwarfs

White dwarfs offer a unique opportunity to search nearby stellar systems for signs of life, but the habitable zone around these stars is still poorly understood. Since white dwarfs are compact stars with low luminosity, any planets in their habitable zone should be tidally locked, like planets around M-dwarfs. Unlike planets around M-dwarfs, however, habitable white dwarf planets have to rotate very rapidly, with orbital periods ranging from hours to several days. Here we use the ExoCAM Global Climate Model (GCM) to investigate the inner edge of the habitable zone (HZ) around white dwarfs. Our simulations show habitable planets with ultrashort orbital periods ($P\lesssim$1 day) enter a ``bat rotation" regime, which differs from typical atmospheric circulation regimes around M dwarfs. Bat rotators feature mean equatorial subrotation and a displacement of the surface's hottest regions from the equator towards the midlatitudes. We qualitatively explain the onset of bat rotation using shallow water theory. The resulting circulation shifts increase dayside cloud cover and decrease stratospheric water vapor, expanding the white dwarf habitable zone by $\sim$50\% compared to estimates based on 1D models. The James Webb Space Telescope (JWST) should be able to quickly characterize bat rotators around nearby white dwarfs thanks to their distinct thermal phase curves. Our work underlines that tidally locked planets on ultrashort orbits may exhibit unique atmospheric dynamics, and guides future habitability studies of white dwarf systems.

astro-ph.EP↗

arXiv2024

Potential Surface Ice Distribution on Close-in Terrestrial Exoplanets around M dwarfs

Previous studies suggested that surface ice could be distributed on close-in terrestrial exoplanets around M-dwarfs if heat redistribution on the planets is very inefficient. In general, orbital and atmospheric parameters play an important role in the climate on terrestrial planets, including the cold-trap region where the permanent surface water reservoir can potentially be distributed. Here, we develop a simple coupled land-atmosphere model to explore the potential surface ice distribution on close-in terrestrial planets with various orbital and atmospheric parameters, assuming that the planets are airless or have a thin \ce{N2} atmosphere. We find that the most significant factors in deciding the surface cold trap region are the spin-orbit ratio and obliquity. The incident stellar flux and the surface pressure play a limited role in the thin \ce{N2} simulations for incident flux smaller than Mercury's and surface pressure lower than 10$^4$ Pa. Our result illustrates the possible distribution of surface ice on arid terrestrial planets and can help to understand the climate of these exoplanets.

astro-ph.EP↗

arXiv2024

Hydrologic Cycle Weakening in Hothouse Climates

The hydrologic cycle has wide impacts on the ocean salinity and circulation, carbon and nitrogen cycles, and the ecosystem. Under anthropogenic global warming, previous studies showed that the intensification of the hydrologic cycle is a robust feature. Whether this trend persists in hothouse climates, however, is unknown. Here we show in climate models that mean precipitation first increases with rising surface temperature, but the precipitation trend reverses when the surface is hotter than ~320-330 K. This non-monotonic phenomenon is robust to the cause of warming, convection scheme, ocean dynamics, atmospheric mass, planetary rotation, gravity, and stellar spectrum. The weakening occurs because of the existence of an upper limitation of outgoing longwave emission and the continuously increasing shortwave absorption by H2O, and is consistent with atmospheric dynamics featuring the strong increase of atmospheric stratification and dramatic reduction of convective mass flux. These results have wide implications for the climate evolutions of Earth, Venus, and potentially habitable exoplanets.

physics.ao-ph↗

arXiv2024

Ultra-Long Homochiral Graphene Nanoribbons Grown Within h-BN Stacks for High-Performance Electronics

Van der Waals encapsulation of two-dimensional materials within hexagonal boron nitride (h-BN) stacks has proven to be a promising way to create ultrahigh-performance electronic devices. However, contemporary approaches for achieving van der Waals encapsulation, which involve artificial layer stacking using mechanical transfer techniques, are difficult to control, prone to contamination, and unscalable. Here, we report on the transfer-free direct growth of high-quality graphene nanoribbons (GNRs) within h-BN stacks. The as-grown embedded GNRs exhibit highly desirable features being ultralong (up to 0.25 mm), ultranarrow ( < 5 nm), and homochiral with zigzag edges. Our atomistic simulations reveal that the mechanism underlying the embedded growth involves ultralow GNR friction when sliding between AA'-stacked h-BN layers. Using the grown structures, we demonstrate the transfer-free fabrication of embedded GNR field-effect devices that exhibit excellent performance at room temperature with mobilities of up to 4,600 $cm^{2} V^{-1} s^{-1}$ and on-off ratios of up to $10^{6}$. This paves the way to the bottom-up fabrication of high-performance electronic devices based on embedded layered materials.

cond-mat.mes-hall↗

arXiv2024

Preserving Fairness Generalization in Deepfake Detection

Although effective deepfake detection models have been developed in recent years, recent studies have revealed that these models can result in unfair performance disparities among demographic groups, such as race and gender. This can lead to particular groups facing unfair targeting or exclusion from detection, potentially allowing misclassified deepfakes to manipulate public opinion and undermine trust in the model. The existing method for addressing this problem is providing a fair loss function. It shows good fairness performance for intra-domain evaluation but does not maintain fairness for cross-domain testing. This highlights the significance of fairness generalization in the fight against deepfakes. In this work, we propose the first method to address the fairness generalization problem in deepfake detection by simultaneously considering features, loss, and optimization aspects. Our method employs disentanglement learning to extract demographic and domain-agnostic forgery features, fusing them to encourage fair learning across a flattened loss landscape. Extensive experiments on prominent deepfake datasets demonstrate our method's effectiveness, surpassing state-of-the-art approaches in preserving fairness during cross-domain deepfake detection. The code is available at https://github.com/Purdue-M2/Fairness-Generalization

cs.CV↗

arXiv2024

Probing charge redistribution at the interface of self-assembled cyclo-P5 pentamers on Ag(111)

Phosphorus pentamer (cyclo-P5-) ions are unstable in nature but can be synthesized at the Ag(111) surface. Unlike monolayer black phosphorous, little is known about their electronic properties when in contact with metal electrodes, although this is crucial for future applications. Here we characterize the atomic structure of cyclo-P5 assembled on Ag(111) using atomic force microscopy with functionalized tips and density functional theory. Combining force and tunneling spectroscopy, we find that a strong charge transfer induces an inward dipole moment at the cyclo-P5/Ag interface as well as the formation of an interface state. We probe the image potential states by field-effect resonant tunneling and quantify the increase of the local change of work function of 0.46 eV at the cyclo-P5 assembly. Our results suggest that the high-quality of the cyclo-P5/Ag interface might serve as a prototypical system for electric contacts in phosphorus-based semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2023

Corrosion-resistant aluminum alloy design through machine learning combined with high-throughput calculations

Efficiently designing lightweight alloys with combined high corrosion resistance and mechanical properties remains an enduring topic in materials engineering. To this end, machine learning (ML) coupled ab-initio calculations is proposed within this study. Due to the inadequate accuracy of conventional stress-strain ML models caused by corrosion factors, a novel reinforcement self-learning ML algorithm (accuracy R2 >0.92) is developed. Then, a strategy that integrates ML models, calculated energetics and mechanical moduli is implemented to optimize the Al alloys. Next, this Computation Designed Corrosion-Resistant Al alloy is fabricated that verified the simulation. The performance (elongation reaches ~30%) is attributed to the H-captured Al-Sc-Cu phases (-1.44 eV H-1) and Cu-modified η/η' precipitation inside the grain boundaries (GBs). The developed Al-Mg-Zn-Cu interatomic potential (energy accuracy 6.50 meV atom-1) proves the cracking resistance of the GB region enhanced by Cu-modification. Conceptually, our strategy is of practical importance for designing new alloys exhibiting corrosion resistance and mechanical properties.

cond-mat.mtrl-sci↗

arXiv2023

Synthesizing Black-box Anti-forensics DeepFakes with High Visual Quality

DeepFake, an AI technology for creating facial forgeries, has garnered global attention. Amid such circumstances, forensics researchers focus on developing defensive algorithms to counter these threats. In contrast, there are techniques developed for enhancing the aggressiveness of DeepFake, e.g., through anti-forensics attacks, to disrupt forensic detectors. However, such attacks often sacrifice image visual quality for improved undetectability. To address this issue, we propose a method to generate novel adversarial sharpening masks for launching black-box anti-forensics attacks. Unlike many existing arts, with such perturbations injected, DeepFakes could achieve high anti-forensics performance while exhibiting pleasant sharpening visual effects. After experimental evaluations, we prove that the proposed method could successfully disrupt the state-of-the-art DeepFake detectors. Besides, compared with the images processed by existing DeepFake anti-forensics methods, the visual qualities of anti-forensics DeepFakes rendered by the proposed method are significantly refined.

cs.CV↗

arXiv2023

Intralayer Negative Poisson's Ratio in Two-Dimensional Black Arsenic by Strain Engineering

Negative Poisson's ratio as the anomalous characteristic generally exists in artificial architectures, such as re-entrant and honeycomb structures. The structures with negative Poisson's ratio have attracted intensive attention due to their unique auxetic effect and many promising applications in shear resistant and energy absorption fields. However, experimental observation of negative Poisson's ratio in natural materials barely happened, although various two-dimensional layered materials are predicted in theory. Herein, we report the anisotropic Raman response and the intrinsic intralayer negative Poisson's ratio of two-dimensional natural black arsenic (b-As) via strain engineering strategy. The results were evident by the detailed Raman spectrum of b-As under uniaxial strain together with density functional theory calculations. It is found that b-As was softer along the armchair than zigzag direction. The anisotropic mechanical features and van der Waals interactions play essential roles in strain-dependent Raman shifts and negative Poisson's ratio in the natural b-As along zigzag direction. This work may shed a light on the mechanical properties and potential applications of two-dimensional puckered materials.

cond-mat.mtrl-sci↗

arXiv2023

Magic momenta and three dimensional Landau levels from a three dimensional graphite moiré superlattice

Twisted bilayer graphene (TBG) and other quasi-two-dimensional moiré superlattices have attracted significant attention due to the emergence of various correlated and topological states associated with the flat bands in these systems. In this work, we theoretically explore the physical properties of a new type of \textit{three dimensional graphite moiré superlattice}, the bulk alternating twisted graphite (ATG) system with homogeneous twist angle, which is grown by in situ chemical vapor decomposition method. Compared to TBG, the bulk ATG system is bestowed with an additional wavevector degrees of freedom due to the extra dimensionality. As a result, we find that when the twist angle of bulk ATG is smaller than twice of the magic angle of TBG, there always exist ``magic momenta" at which the in-plane Fermi velocities of the moiré bands vanish. Moreover, topologically distinct flat bands of TBG at different magic angles can even co-exist at different out-of-plane wavevectors in a single bulk ATG system. Most saliently, when the twist angle is relatively large, exactly dispersionless three dimensional zeroth Landau level would emerge in the bulk ATG, which may give rise to robust three dimensional quantum Hall effects over a large range of twist angles.

cond-mat.mes-hall↗