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Phase-field simulations of the effect of temperature and interface for zirconiumδ-hydrides

Chinese Physics B 2024年第4期33卷 701-710页

作者：陈子航盛杰刘瑜施小明黄厚兵许可王越超武帅孙博刘海风宋海峰 School of Materials science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research institute of Multidisciplinary science Beijing Institute of TechnologyBeijing 100081China Laboratory of computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics University of Science and Technology BeijingBeijing 100083China

Hydride precipitation in zirconium cladding materials can damage their integrity and *** temperature and material defects have a significant effect on the dynamic growth of *** this study,we have developed a phasefield model based on the assumption of elastic behaviour within a specific temperature range(613 K-653 K).This model allows us to study the influence of temperature and interfacial effects on the morphology,stress,and average growth rate of zirconium *** results suggest that changes in temperature and interfacial energy influence the length-to-thickness ratio and average growth rate of the hydride *** ultimate determinant of hydride orientation is the loss of interfacial coherency,primarily induced by interfacial dislocation defects and quantifiable by the mismatch degree *** escalation in interfacial coherency loss leads to a transition of hydride growth from horizontal to vertical,accompanied by the onset of redirection ***,redirection occurs at a critical mismatch level,denoted as qc,and remains unaffected by variations in temperature and interfacial ***,this redirection leads to an increase in the maximum stress,which may influence the direction of hydride crack *** research highlights the importance of interfacial coherency and provides valuable insights into the morphology and growth kinetics of hydrides in zirconium alloys.

关键词： zirconium hydride phase-field method temperature effect mismatch degree

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Ionization Potential Depression Model for Warm/Hot and Dense Plasmas

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Chinese Physics Letters 2024年第8期41卷 45-57页

作者： Chensheng Wu Fuyang Zhou Jun Yan Xiang Gao Yong Wu Chunhua Zeng Jianguo Wang institute of Applied Physics and computational Mathematics Beijing 100088China Faculty of science Kunming University of Science and TechnologyKunming 650500China

For warm/hot and dense plasmas(WDPs),ionization potential depression(IPD)plays a crucial role in determining its ionization balance and understanding the resultant microscopic plasma properties.A sophisticated and unified IPD model is necessary to resolve those existing discrepancies between theoretical and experimental ***,the applicability of those widely used IPD models nowadays is limited,especially for the nonlocal thermodynamic equilibrium(non-LTE)dense plasma produced by short-pulse *** this work,we propose an IPD model that considers inelastic atomic processes,in which three-body recombination and collision ionization processes are found to play a crucial role in determining the electron distribution and IPD for a *** IPD model is validated by reproducing latest experimental results of Al plasmas with a wide-range condition of 70 eV–700 eV temperature and 0.2–3 times solid density,as well as a typical non-LTE system of hollow Al *** is demonstrated that the present IPD model has a significant temperature dependence due to the consideration of the inelastic collision *** a lower computational cost and wider application range of plasma conditions,the proposed model is expected to provide a promising tool to study the ionization balance and the atomic processes,as well as the related radiation and particle transports properties of the WDP.

关键词： properties. collision determining

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Topological magnetoelectric response in ferromagnetic axion insulators

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National science Review 2024年第2期11卷 8-14页

作者： Yuhao Wan Jiayu Li Qihang Liu Department of Physics and Shenzhen institute for Quantum science and Engineering(SIQSE) Southern University of Science and Technology Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices Southern University of Science and Technology Guangdong Provincial Key Laboratory for computational science and Material Design Southern University of Science and Technology

The topological magnetoelectric effect （TME） is a hallmark response of the topological field theory,which provides a paradigm shift in the study of emergent topological ***,its direct observation is yet to be realized due to the demanding magnetic configuration required to gap all surface ***,we theoretically propose that axion insulators with a simple ferromagnetic configuration,such as the MnBi2Te4/（Bi2Te3）nfamily,provide an ideal playground to realize the *** the designed triangular prism geometry,all the surface states are magnetically *** a vertical electric field,the surface Hall currents give rise to a nearly half-quantized orbital moment,accompanied by a gapless chiral hinge mode circulating in ***,the orbital magnetization from the two topological origins can be easily distinguished by reversing the electric *** work paves the way for direct observation of the TME in realistic axion-insulator materials.

关键词： topological magnetoelectric effect axion insulator magnetic topological materials

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A FINITE DIFFERENCE METHOD FOR TWO DIMENSIONAL ELLIPTIC INTERFACE PROBLEMS WITH IMPERFECT CONTACT

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Journal of computational Mathematics 2024年第5期42卷 1328-1355页

作者： Fujun Cao Dongfang Yuan Dongxu Jia Guangwei Yuan School of science Inner Mongolia University of Science and TechnologyBaotou 014010Inner MongoliaChina School of science Tianjin University of TechnologyTianjin 300384China Laboratory of computational Physics Institute of Applied Physics and Computational MathematicsP.O.Box 8009Beijing 100088China

In this paper two dimensional elliptic interface problem with imperfect contact is considered,which is featured by the implicit jump condition imposed on the imperfect contact interface,and the jumping quantity of the unknown is related to the flux across the interface.A finite difference method is constructed for the 2D elliptic interface problems with straight and curve interface ***,the stability and convergence analysis are given for the constructed ***,in particular case,it is proved to be *** examples for elliptic interface problems with straight and curve interface shapes are tested to verify the performance of the *** numerical results demonstrate that it obtains approximately second-order accuracy for elliptic interface equations with implicit jump condition.

关键词： Finite difference method Elliptic interface problem Imperfect contact

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Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of Computer science and technology Beijing Institute of Technology Advanced Reasearch institute of Multidisciplinary science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

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FedDAA:a robust federated learning framework to protect privacy and defend against adversarial attack

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Frontiers of Computer science 2024年第2期18卷 107-122页

作者： Shiwei LU Ruihu LI Wenbin LIU Fundamentals Department Air Force Engineering UniversityXi’an 710051China institute of Advanced computational science and technology Guangzhou UniversityGuangzhou 510006China

Federated learning(FL)has emerged to break data-silo and protect clients’privacy in the field of artificial ***,deep leakage from gradient(DLG)attack can fully reconstruct clients’data from the submitted gradient,which threatens the fundamental privacy of *** cryptology and differential privacy prevent privacy leakage from gradient,they bring negative effect on communication overhead or model ***,the original distribution of local gradient has been changed in these schemes,which makes it difficult to defend against adversarial *** this paper,we propose a novel federated learning framework with model decomposition,aggregation and assembling(FedDAA),along with a training algorithm,to train federated model,where local gradient is decomposed into multiple blocks and sent to different proxy servers to complete *** bring better privacy protection performance to FedDAA,an indicator is designed based on image structural similarity to measure privacy leakage under DLG attack and an optimization method is given to protect privacy with the least proxy *** addition,we give defense schemes against adversarial attack in FedDAA and design an algorithm to verify the correctness of aggregated *** results demonstrate that FedDAA can reduce the structural similarity between the reconstructed image and the original image to 0.014 and remain model convergence accuracy as 0.952,thus having the best privacy protection performance and model training *** importantly,defense schemes against adversarial attack are compatible with privacy protection in FedDAA and the defense effects are not weaker than those in the traditional ***,verification algorithm of aggregation results brings about negligible overhead to FedDAA.

关键词： federated learning privacy protection adversarial attacks aggregated rule correctness verification

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Elaborating strengthen mechanism of Pt-Ir solid solution superalloy at finite temperature

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Rare Metals 2024年第3期43卷 1243-1256页

作者： Wei Yu Xiao-Yu Chong Yun-Xuan Zhou Meng-Di Gan Ying-Xue Liang Yan Wei Ai-Min Zhang Chang-Yi Hu Xing-Yu Gao Li Chen Hai-Feng Song Jing Feng Faculty of Materials science and Engineering Kunming University of Science and TechnologyKunming650093China Yunnan Precious Metals Laboratory Co.LTD Kunming650106China Laboratory of computational Physics Institute of Applied Physics and Computational MathematicsBeijing100088China

Pt-Ir alloy is potential superalloys used above 1300℃because of their high strength and creep ***,the ductility of Pt-Ir alloy has rapidly deteriorated with the increase of Ir,resulting in poor *** work quantitatively evaluated the solid solution strengthening(SSS)and grain refinement strengthening(GRS)of Pt-Ir alloy using first-principles calculations combined with experimental ***,the stretching force constants in the second nearest neighbor region(SFC^(2nd))of pure Ir(193.7 eV·nm^(-2))are 3.40 times that of pure Pt(57.0 eV·nm^(-2)),i.e.,the interatomic interaction is greatly enhanced with the increase of Ir content,which leads to the decrease of ductility,and modulus misfit plays a dominant role in ***,the physical mechanisms responsible for the hardness(H_(V))of Pt-Ir alloy,using the power-law-scaled function of electron work function coupled SSS and GRS,are attributed to the electron redistribution caused by different Ir ***,a thorough assessment of the thermodynamic characteristics of Pt-Ir binary alloy was conducted,culminating in development of a mapping model that effectively relates enmposition,temperature and *** results revealed that the compressive strength incrcases with the Ir content,and the highest strength was observed in Pt_(0.25)Ir_(0.75).This study provides valuable insights into the Pt-Ir alloy system.

关键词： Elastic properties Phonon Solid solution strengthen Force constants CALPHAD

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A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

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Tungsten 2024年第2期6卷 304-322页

作者： Chang-Jie Ding Ya-Wei Lei Xiao-Yang Wang Xiao-Lin Li Xiang-Yan Li Yan-Ge Zhang Yi-Chun Xu Chang-Song Liu Xue-Bang Wu Key Laboratory of Materials Physics Institute of Solid State PhysicsHFIPSChinese Academy of SciencesHefei 230031China science Island University of Science and Technology of ChinaHefei 230026China Laboratory of computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China

So far, it has been a challenge for existing interatomic potentials to accurately describe a wide range of physical properties and maintain reasonable efficiency. In this work, we develop an interatomic potential for simulating radiation damage in body-centered cubic tungsten by employing deep potential, a neural network-based deep learning model for representing the potential energy surface. The resulting potential predicts a variety of physical properties consistent with first-principles calculations, including phonon spectrum, thermal expansion, generalized stacking fault energies, energetics of free surfaces, point defects, vacancy clusters, and prismatic dislocation loops. Specifically, we investigated the elasticity-related properties of prismatic dislocation loops, i.e., their dipole tensors, relaxation volumes, and elastic interaction energies. This potential is found to predict the maximal elastic interaction energy between two 1/2 loops better than previous potentials, with a relative error of only 7.6%. The predicted threshold displacement energies are in reasonable agreement with experimental results, with an average of 128 eV. The efficiency of the present potential is also comparable to the tabulated gaussian approximation potentials and modified embedded atom method potentials, meanwhile, can be further accelerated by graphical processing units. Extensive benchmark tests indicate that this potential has a relatively good balance between accuracy, transferability, and efficiency.

关键词： Machine learning Deep learning Interatomic potential Radiation damage Tungsten

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Nonlinear Seebeck and Peltier effects in a Majorana nanowire coupled to leads

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Chinese Physics B 2024年第7期33卷 547-552页

作者：迟锋刘佳付振国刘黎明易子川 School of Electronic and Information Engineering UESTC of ChinaZhongshan InstituteZhongshan 528400China School of science Inner Mongolia University of Science and TechnologyBaotou 014010China institute of Applied Physics and computational Mathematics Beijing 100088China

We theoretically study nonlinear thermoelectric transport through a topological superconductor nanowire hosting Majorana bound states(MBSs) at its two ends, a system named as Majorana nanowire(MNW). We consider that the MNW is coupled to the left and right normal metallic leads subjected to either bias voltage or temperature gradient. We focus our attention on the sign change of nonlinear Seebeck and Peltier coefficients induced by mechanisms related to the MBSs, by which the possible existence of MBSs might be proved. Our results show that for a fixed temperature difference between the two leads, the sign of the nonlinear Seebeck coefficient(thermopower) can be reversed by changing the overlap amplitude between the MBSs or the system equilibrium temperature, which are similar to the cases in linear response regime. By optimizing the MBS–MBS interaction amplitude and system equilibrium temperature, we find that the temperature difference may also induce sign change of the nonlinear thermopower. For zero temperature difference and finite bias voltage, both the sign and magnitude of nonlinear Peltier coefficient can be adjusted by changing the bias voltage or overlap amplitude between the MBSs. In the presence of both bias voltage and temperature difference, we show that the electrical current at zero Fermi level and the states induced by overlap between the MBSs keep unchanged, regardless of the amplitude of temperature difference. We also find that the direction of the heat current driven by bias voltage may be changed by weak temperature difference.

关键词： quantum dot nonlinear Seebeck coefficient Peltier coefficient Majorana bound states sign change

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Fast Cross-Platform Binary Code Similarity Detection Framework Based on CFGs Taking Advantage of NLP and Inductive GNN

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Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU School of Computer science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Training Semantics Process control Binary codes Feature extraction Natural language processing Graph neural networks

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