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Revealing the effect of external pressure on solid-electrolyte interphase and lithium plating in lithium-ion batteries

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Journal of Energy Chemistry 2025年第3期102卷 734-744页

作者： Rui Xiong Haocheng Li Bing-Ang Mei Hongwen He Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 00081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external *** force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external ***,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium *** results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of ***,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge *** integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate *** study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.

关键词： Lithium-ion batteries Overcharge Lithium plating Electrochemical mechanics Synergistic effects

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Enhanced electrode-level diagnostics for lithium-ion battery degradation using physics-informed neural networks

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Journal of Energy Chemistry 2025年第5期104卷 618-627页

作者： Rui Xiong Yinghao He Yue Sun Yanbo Jia Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation *** models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing ***-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational *** study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity,electrode capacities,and lithium inventory *** proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public *** results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 *** demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.

关键词： Lithium-ion batteries Electrode level Ageing diagnosis Physics-informed neural network Convolutional neural networks

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1/f noise of a nanopillar tunnel-magnetoresistance sensor originating from a wide distribution of bath correlation times

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Physical Review Applied 2024年第1期22卷 014032-014032页

作者： Hiroshi Imamura Hiroko Arai Rie Matsumoto Toshiki Yamaji Research Center for Emerging Computing Technologies (RCECT)

The tunnel magnetoresistance (TMR) sensor is a highly sensitive magnetic field sensor that is expected to be applied in various fields, such as magnetic recording, industrial sensing, and biomedical sensing. To improve the detection capability of TMR sensors in the low-frequency regime, it is necessary to suppress the 1/f noise. We theoretically study the 1/f noise of a tiny TMR sensor using the macrospin model. Starting from the generalized Langevin equation, the 1/f noise power spectrum and the Hooge parameter are derived. The calculated Hooge parameter of a tiny TMR sensor is much smaller than that of a conventional TMR sensor with large junction area. The results provide a new perspective on magnetic 1/f noise and will be useful for improving TMR sensors.

关键词： Magnetometry Noise Spintronics Tunneling magnetoresistance Nanostructures Solid-state detectors Medical imaging

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Studying the expressiveness and performance of parallelization abstractions for linear pipelines 14

Studying the expressiveness and performance of parallelizati...

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14th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2023 - Part of PPoPP 2023

作者： Mastoras, Aristeidis Yzelman, Albert-Jan N. Computing Systems Laboratory Zurich Research Center Huawei Technologies Zurich Switzerland

ISBN: (纸本)9798400701153

Semi-automatic parallelization provides abstractions that simplify the programming effort and allow the user to make decisions that cannot be made by tools. However, abstractions for general-purpose systems usually do not carry sufficient knowledge about the structure of the program, and thus parallelization with them may lead to poor *** this paper, we present a popular class of programs, called linear pipelines, that cannot be easily and efficiently parallelized with general-purpose abstractions. We discuss the difficulties and inefficiencies of parallelizing linear pipelines with general-purpose abstractions, and we explain how pattern-specific abstractions overcome these problems. We present the properties of linear pipelines that should be described with pattern-specific abstractions and how these properties are exploited by the state of the art. In addition, we discuss the importance of exposing the performance parameters and how they are combined by pattern-specific knowledge. We claim that designing pattern-specific abstractions for general-purpose programming models is one way to simplify parallel programming and improve performance without sacrificing any expressive power. Consequently, we propose possible pattern-specific extensions to general-purpose parallel programming models, e.g., OpenMP, to support easy and efficient parallelization of linear pipelines. © 2023 ACM.

关键词： Parallel programming

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Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 647-660页

作者： Tian, Jiao Tsai, Pei-Wei Zhang, Kai Cai, Xinyi Xiao, Hongwang Yu, Ke Zhao, Wenyu Chen, Jinjun Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia Center for Health Computing Research Beijing Academy of Artificial Intelligence Beijing100084 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Applying sparsity- and overfitting-aware eXtreme Gradient Boosting (XGBoost) for classification in federated learning allows many participants to train a series of trees collaboratively. Since various local multiclass distributions and global aggregation diversity, model performance plummets as convergence slowly and accuracy decreases. Worse still, neither the participants nor the server can detect this problem and make timely adjustments. In this article, we provide a new local-global class imbalance inconsistency quantification and utilize softmax as the activation and focal loss, a dynamically scaled cross-entropy loss, in federated XGBoost to mitigate local class imbalance. Moreover, we propose a simple but effective hyperparameter determination strategy based on local data distribution to adjust the sample weights among noncommunicating participants, synergetic focal loss, to solve the inconsistency of local and global class imbalance, a unique characteristic of federated learning. This strategy is perfectly integrated into the original classification algorithm. It requires no additional detectors or information transmission. Furthermore, a dynamical for loop is designed to capture an optimum hyperparameter combination. Finally, we conduct comprehensive tabular- and image-based experiments to show that synergetic focal loss used in federated XGBoost achieves faster convergency and significant accuracy improvement. Simulation results prove the effectiveness of the proposed principle of configuring sample weights. © 2023 IEEE.

关键词： Heuristic algorithms

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Adaptive Mesh Refinement for Electromagnetic Simulation 11th

Adaptive Mesh Refinement for Electromagnetic Simulation

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11th International Conference on Numerical Geometry, Grid Generation, and Scientific computing, NUMGRID 2022

作者： Belokrys-Fedotov, Alexey Garanzha, Vladimir Kamenski, Lennard Chikitkin, Alexandr Pesnya, Evgeniy Aseev, Nikita Vorobyev, Andrey Dorodnicyn Computing Center FRC CSC RAS Moscow Russia Moscow Russia Huawei Technologies Shenzhen China

ISBN: (纸本)9783031596513

We consider problems related to initial meshing and adaptive mesh refinement (AMR) for the electromagnetic simulation of various structures. The quality of the initial mesh and the performance of the adaptive refinement are of great importance for the finite element solution of the Maxwell equations, since they directly affect the accuracy and the computational time. In this paper, we describe the complete meshing workflow, which allows the simulation of arbitrary structures. Test simulations confirm that the presented approach allows to reach the quality of the industrial simulation software. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Maxwell equations

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Integration of equations of acoustics of inhomogeneous media

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Theoretical and Mathematical Physics 2025年第2期223卷 770-781页

作者： Kaptsov, O. V. Federal Research Center for Information and Computing Technologies Novosibirsk Russia

We propose two approaches to integrating linear acoustic equations in inhomogeneous media. The first is based on the Laplace cascade method. For one-dimensional nonstationary equations, new solutions are obtained that depend on two arbitrary functions. These solutions are generalizations of relatively undistorted waves. In the two-dimensional case, conformal maps are used that allow reducing some equations with variable coefficients to equations with constant coefficients. Special three-dimensional equations can also be transformed to a wave equation.

关键词： acoustic equations Laplace cascade method conformal maps exact solutions

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Bayesian blacksmithing:discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys

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npj Computational Materials 2024年第1期10卷 1545-1557页

作者： Jacob Startt Megan J.McCarthy Mitchell A.Wood Sean Donegan Rémi Dingreville Center for Integrated Nanotechnologies Sandia National LaboratoriesAlbuquerqueNM87185USA Center for Computing Research Computational Multiscale DepartmentSandia National LaboratoriesAlbuquerqueNM87185USA Manufacturing and Industrial Technologies Division Air Force Research LaboratoryWright Patterson AFBOH45433USA

Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between *** study introduces a multiobjective Bayesian optimization approach guiding molecular dynamics simulations for discovering high-performance refractory alloys with both targeted intrinsic static thermomechanical properties and also deformation mechanisms occurring during dynamic *** objective functions are aiming for excellent thermomechanical stability via a high bulk modulus,a low thermal expansion,a high heat capacity,and for a resilient deformation mechanism maximizing the retention of the BCC phase after shock *** two optimization procedures,we show that the Pareto-optimal solutions are confined to a small performance space when the property objectives display a cooperative ***,the Pareto front is much broader in the performance space when these properties have antagonistic *** functional theory simulations validate these findings and unveil underlying atomic-bond changes driving property improvements.

关键词： deformation alloys thermomechanical

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Cambricon-DG: An Accelerator for Redundant-Free Dynamic Graph Neural Networks Based on Nonlinear Isolation 31

Cambricon-DG: An Accelerator for Redundant-Free Dynamic Grap...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yue, Zhifei Song, Xinkai Liu, Tianbo Hu, Xing Zhang, Rui Du, Zidong Li, Wei Guo, Qi Chen, Tianshi University of Science and Technology of China China Institute of Computing Technology Chinese Academy of Sciences SKLP China Shanghai Innovation Center for Processor Technologies SHIC China Cambricon Technologies China

ISBN: (纸本)9798331506476

Dynamic Graph Neural Networks (DGNNs) have demonstrated significant potential in handling temporal graph-structured data. Typical DGNNs combine GNNs to process the structural information of graph and RNNs to capture the dynamic temporal information of graphs. However, current GNN accelerators process DGNNs by treating graph snapshots as static entities, leading to substantial redundant computations and memory accesses. While some DGNN accelerators have attempted to mitigate this redundancy, they face the challenge of maintaining accuracy for incremental processing due to the nonlinear activation function in GNNs. To address these issues, we propose a novel nonlinear isolation mechanism for incremental processing of DGNNs, which eliminates all redundant operations without accuracy loss. Additionally, we introduce a vertex-wise computation scheme for RNNs to minimize redundancy and reduce off-chip memory access. To validate the effectiveness of the mechanism, we implemented software optimizations on GPUs, achieving an 8.53 × performance improvement over full-graph computation schemes. To address the inefficiency of graph topology operations on GPUs, we propose the Cambricon-DG accelerator, which features a dedicated hardware pipeline, an efficient topology management engine for graph traversal and topology sorting, and a hybrid computation engine that supports both GNN and RNN computations, thus improving hardware utilization. We evaluate Cambricon-DG on seven real-world dynamic graph datasets using five representative DGNN models. The results show that Cambricon-DG achieves an average speedup of 49.73 ×, 12.15 ×, and 7.03 ×, and an average energy saving of 41.31 ×, 9.47 ×, and 5.23 × over the state-of-the-art static/dynamic GNN accelerators I-GCN, RACE, and DeltaGNN, respectively. © 2025 IEEE.

关键词： Graph neural networks

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Towards Privacy in Decentralized IoT:A Blockchain-Based Dual Response DP Mechanism

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Big Data Mining and Analytics 2024年第3期7卷 699-717页

作者： Kai Zhang Pei-Wei Tsai Jiao Tian Wenyu Zhao Xinyi Cai Longxiang Gao Jinjun Chen Department of Computing Technologies Swinburne University of TechnologyMelbourne 3122Australia School of Computer Science and Technology Xinjiang UniversityUrumchi 830000China Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China

Differential Privacy(DP)stands as a secure and efficient mechanism for privacy preservation,offering enhanced data utility without compromising computational *** adaptability is evidenced by its integration into blockchain-based Internet of Things(IoT)contexts,including smart wearables,smart homes,***,a notable vulnerability surfaces in decentralized environments where existing DP mechanisms falter in withstanding collusion *** vulnerability stems from the absence of an efficient strategy to synchronize the privacy budget consumption and historical query information among all network *** can exploit this weakness,collaborating to inject a substantial volume of queries simultaneously into disparate blockchain nodes to extract more precise *** address this issue,we propose a novel dual response DP mechanism to preserve privacy in blockchain-based IoT *** encompasses both direct and indirect response strategies,enabling an adaptive response to external queries,aiming to provide better data utility while preserving ***,this mechanism can synchronize historical query information and privacy budget consumption within the blockchain network to prevent privacy *** employ Relative Error(RE),Mean Square Error(MSE),and privacy budget consumption as evaluation metrics to measure the performance of the proposed *** outcomes substantiate that the proposed mechanism can adapt to blockchain networks well,affirming its capacity for privacy and great utility.

关键词： differential privacy dual response strategies collusion attack blockchain

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