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Observation of magnetic droplets in magnetic tunnel junctions

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arXiv 2020年

作者： Shi, Kewen Cai, Wenlong Jiang, Sheng Zhu, Daoqian Cao, Kaihua Guo, Zongxia Wei, Jiaqi Du, Ao Li, Zhi Huang, Yan Yin, Jialiang Åkerman, Johan Zhao, Weisheng Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Department of Physics University of Gothenburg Gothenburg412 96 Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology Electrum 229 KistaSE-16440 Sweden

Magnetic droplets, a class of highly non-linear magnetodynamical solitons, can be nucleated and stabilized in nanocontact spin-torque nano-oscillators where they greatly increase the microwave output power. Here, we experimentally demonstrate magnetic droplets in magnetic tunnel junctions (MTJs). The droplet nucleation is accompanied by a power increase of over 300 times compared to its ferromagnetic resonance modes. The nucleation and stabilization of droplets are ascribed to the double-CoFeB free layer structure in the all-perpendicular MTJ which provides a low Zhang-Li torque and a high pinning field. Our results enable better electrical sensitivity in the fundamental studies of droplets and show that the droplets can be utilized in MTJ-based applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Spin dynamics

Highly Selective Electrocatalytic Olefin Hydrogenation in Aqueous Solution

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Angewandte Chemie 2023年第41期135卷

作者： Chengyu Xing Prof. Yurui Xue Xuchen Zheng Dr. Yang Gao Siao Chen Prof. Yuliang Li Beijing Advanced Innovation Centre for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 P. R. China CAS Key Laboratory of Organic Solids Institute of Chemistry Chinese Academy of Sciences Beijing 100190 P. R. China Contribution: Data curation (lead) Formal analysis (lead) Methodology (lead) Validation (lead) Writing - original draft (equal) Shandong Provincial Key Laboratory for Science of Material Creation and Energy Conversion Science Center for Material Creation and Energy Conversion Science School of Chemistry and Chemical Engineering Shandong University Jinan 250100 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Methodology (lead) Supervision (lead) Writing - original draft (lead) University of Chinese Academy of Sciences Beijing 100190 P. R. China Contribution: Formal analysis (lead) Methodology (lead) Contribution: Formal analysis (equal) Methodology (equal)

Selective hydrogenation of olefins with water as the hydrogen source at ambient conditions is still a big challenge in the field of catalysis. Herein, the electrocatalytic hydrogenation of purely aliphatic and functionalized olefins was achieved by using graphdiyne based copper oxide quantum dots (Cu x O/GDY) as cathodic electrodes and water as the hydrogen source, with high activity and selectivity in aqueous solution at high current density under ambient temperature and pressure. In particular, the sp-/sp 2 -hybridized graphdiyne catalyst allows the selective hydrogenation of cis-trans isomeric olefins. The chemical and electronic structure of the GDY results in the incomplete charge transfer between GDY and Cu atoms to optimize the adsorption/desorption of the reaction intermediates and results in high reaction selectivity and activity for hydrogenation reactions.

关键词： Atomic Active Sites Electrocatalysis Graphdiyne Highly Selectivity Hydrogenation

Forecasting Thailand’s Precipitation with Cascading Model of CNN and GRU

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Forecasting Thailand’s Precipitation with Cascading Model o...

International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in Thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. There are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in Thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. The experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in Thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

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Word2Cluster: A New Multi-Label Text Clustering Algorithm wi...

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Kaili Mao Jianwei Niu Xuefeng Liu Shui Yu Longbo Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China China aerospace science and industry corporation China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.

关键词： Clustering algorithms Biological system modeling Merging Task analysis Computer science Technological innovation Natural language processing

Grain-growth Inhibitor with Three-section-sintering for Highly Dispersed Single-crystal NCM90 Cubes

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Angewandte Chemie 2023年第2期136卷

作者： Dr. Hao Huang Hongjian Zhu Dr. Jian Gao Dr. Jiantao Wang Prof. Minhua Shao Prof. Weidong Zhou Beijing Advanced Innovation Center for Soft Matter Science and Engineering State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing 100029 China Contribution: Data curation (lead) Investigation (lead) Writing - original draft (lead) Contribution: Investigation (supporting) China Automotive Battery Research Institute Co. Ltd. Beijing 101407 China Contribution: Methodology (supporting) Supervision (supporting) Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong 999077 China Contribution: Methodology (supporting) Software (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Supervision (lead) Writing - original draft (equal) Writing - review & editing (lead)

Single crystallization of LiNi x Co y Mn 1-x-y O 2 (NCM) is currently an effective strategy to improve the cycling life of NCM cathode, owing to the reduced surface area and enhanced mechanical strength, but the application of single crystal NCM(SC-NCM) is being hindered by severe particle agglomeration and poor C-rate performance. Here, a strategy of three-section-sintering(TSS) with the presence of grain-growth inhibitor was proposed, in which, the TSS includes three sections of phase-formation, grain-growth and phase-preservation. While, the addition of MoO 3 inhibits the grain growth and restrains the particle agglomeration. With the help of TSS and 1 mol % of MoO 3 , highly dispersed surface Mo-doped SC-NCM(MSC-NCM) cubes are obtained with the average diameter of 1.3 μm. Benefiting from the surface Mo-doping and the reduced surface energy, the Li + -migration preferred (1 0 4) crystalline facet is exposed as the dominant plane in MSC-NCM cubes, because of which, C-rate performance is significantly improved compared with the regular SC-NCM. Furthermore, this preparation strategy is compatible well with the current industrial production line, providing an easy way for the large-scale production of SC-NCM.

关键词： Cathode Cube Grain Growth Inhibitor Lithium-Ion Batteries Single Crystal

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

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arXiv 2025年

作者： Zhang, Yanbo Khan, Sumeer A. Mahmud, Adnan Yang, Huck Lavin, Alexander Levin, Michael Frey, Jeremy Dunnmon, Jared Evans, James Bundy, Alan Dzeroski, Saso Tegner, Jesper Zenil, Hector Allen Discovery Center Tufts University MedfordMA United States Living Systems Lab KAUST Thuwal Saudi Arabia Intelligent Infrastructure Team Network Rail United Kingdom Department for AI in Society Science and Technology Zuse Institute Berlin Germany NVIDIA Research United States Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Thuwal23952 Saudi Arabia Pasteur Labs BrooklynNY United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Chemistry University of Southampton University Road Hampshire Southampton United Kingdom Department of Biomedical Data Science Stanford University StanfordCA United States Knowledge Lab Department of Sociology University of Chicago IL United States Santa Fe Institute NM United States School of Informatics The University of Edinburgh United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Thuwal23955-6900 Saudi Arabia Unit of Computational Medicine Department of Medicine Center for Molecular Medicine Karolinska Institutet Karolinska University Hospital L8:05 StockholmSE-171 76 Sweden Thuwal23955-6900 Saudi Arabia Science for Life Laboratory Tomtebodavagen 23A SolnaSE-17165 Sweden Algorithmic Dynamics Lab Research Departments of Biomedical Computing and Digital Twins School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom King’s Institute for Artificial Intelligence London United Kingdom The Alan Turing Institute London United Kingdom Oxford Immune Algorithmics Oxford University Innovation London Institute for Healthcare Engineering London United Kingdom Cancer Interest Group Francis Crick Institute London United Kingdom

With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design, data analysis, and workflows, particularly in chemistry and biology. However, challenges such as hallucinations and reliability persist. In this contribution, we review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics. The transition to AI-driven science raises ethical questions about creativity, oversight, and responsibility. With careful guidance, LLMs could evolve into creative engines, driving transformative breakthroughs across scientific disciplines responsibly and effectively. However, the scientific community must also decide how much it leaves to LLMs to drive science, even when associations with ‘reasoning’, mostly currently undeserved, are made in exchange for the potential to explore hypothesis and solution regions that might otherwise remain unexplored by human exploration alone. Copyright © 2025, The Authors. All rights reserved.

关键词： Plants (botany)

Fine-grained event categorization with heterogeneous graph convolutional networks

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

Isospin-density dependent pairing from infinite nuclear matter to finite nuclei

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arXiv 2020年

作者： Meng, Xu Zhang, Shisheng Guo, Lin Geng, Lisheng Cao, Ligang School of Physics Beihang University Beijing100191 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihan China School of Physics and Microelectronics Zhengzhou University ZhengzhouHenan450001 China College of Science and Technology Beijing Normal University Beijing100875 China

The effective isospin-density dependent pairing interaction (P1) [S. S. Zhang, U. Lombardo and E. G. Zhao, Sci. Chin. Phys. Mech. Astro. 54, 236 (2011)] extracted from neutron pairing gaps for 1S0 in asymmetric nuclear matter calculations [S. S. Zhang, L. G. Cao, U. Lombardo, et al. Phys. Rev. C 81, 044313 (2010)] is employed to study the bulk properties of Ca, Ni, Zr and Sn isotopes. The odd-even mass (OEM) staggering is calculated by the Skyrme Hartree-Fock plus BCS method (SHF + BCS) with the SkP interaction. For comparison, we study two other types of isovector effective pairing interactions. One is also extracted from pairing gaps of infinite nuclear matter by the Brueckner-Hartree-Fock (BHF) method but for free spectrum (P2). The other is obtained by fitting the empirical OEM (P3). An isoscalar effective pairing interaction (P4) is also adopted which is determined by fitting the empirical OEM. We find that interaction P1 can better describe the OEM staggering of Ni, Zr and Sn isotopes by 14.3%, 41%, 30.4% compared with interaction P2, in terms of root mean square deviations to the empirical OEM, respectively. On the other hand, the performance of P1 and P2 is comparable for Ca isotopes. For Ca and Ni isotopes, P1 behaves similarly as P3, but for Zr isotopes P1 is better than P3 by ∼ 34%. Among the four pairing interactions studied, P4 performs the worst. Therefore, one may conclude that for neutron pairings in finite nuclei, the isovector pairings are preferred than the isoscalar one. It is quite interesting to note that the pairing interaction P1 extracted from nuclear matter calculations can describe pairing gaps of finite nuclei as well as or even better than the interaction P3 directly fitted to finite nuclei. Copyright © 2020, The Authors. All rights reserved.

关键词： Isotopes

Analytical Impact Time Guidance Law with Reduced Filed-of-View Limit

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Analytical Impact Time Guidance Law with Reduced Filed-of-Vi...

第三十八届中国控制会议

作者： Kewei Zhong Tuo Han Qinglei Hu Yong Xi Shanghai Aerospace Control Technology Institute School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big-Data Based Precision Medicine Beihang University National University of Defense Technology

An analytical guidance law considering impact time constraint and reduced filed-of-view(FOV) limit is proposed. To achieve the desired constraints, the relative range profile is designed as a quadratic polynomial in terms of an auxiliary variable defined by the seeker look angle. Specifically, the reduced FOV is ensured by guaranteeing the positive first-order derivative of the developed range profile such that the seeker's look angle decreases monotonically from its initial value to zero at the collision point. The impact time and zero miss distance are met by integrating the range profiles with boundary constraints, and thus the analytical guidance law is derived by transforming the guidance model in terms of the auxiliary variable. Finally, numerical simulations are conducted to validate effectiveness of the guidance law.

关键词： Analytical guidance Impact time Filed-of-view limit