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arXiv 2022年

作者： Angelopoulos, V. Zhang, X.-J. Artemyev, A.V. Mourenas, D. Tsai, E. Wilkins, C. Runov, A. Liu, J. Turner, D.L. Li, W. Khurana, K. Wirz, R.E. Sergeev, V.A. Meng, X. Wu, J. Hartinger, M.D. Raita, T. Shen, Y. An, X. Shi, X. Bashir, M.F. Shen, X. Gan, L. Qin, M. Capannolo, L. Ma, Q. Russell, C.L. Masongsong, E.V. Caron, R. He, I. Iglesias, L. Jha, S. King, J. Kumar, S. Le, K. Mao, J. McDermott, A. Nguyen, K. Norris, A. Palla, A. Roosnovo Tam, J. Xie, E. Yap, R.C. Ye, S. Young, C. Adair, L.A. Shaffer, C. Chung, M. Cruce, P. Lawson, M. Leneman, D. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B.W. Eldin, S. Fitzgibbon, L. Flemming, A. Frederick, D.M. Gilbert, A. Hesford, B. Krieger, R. Lian, K. McKinney, E. Miller, J.P. Pedersen, C. Qu, Z. Rozario, R. Rubly, M. Seaton, R. Subramanian, A. Sundin, S.R. Tan, A. Thomlinson, D. Turner, W. Wing, G. Wong, C. Zarifian, A. Earth Planetary and Space Sciences Department Institute of Geophysics and Planetary Physics University of California Los Angeles Los AngelesCA90095 United States University of Texas at Dallas RichardsonTX75080 United States CEA DAM DIF Arpajon France Atmospheric and Oceanic Sciences Departments University of California Los AngelesCA United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Astronomy Center for Space Physics Boston University BostonMA United States Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States School of Mechanical Industrial Manufacturing Engineering Oregon State University CorvallisOR97331 United States University of St. Petersburg St. Petersburg Russia Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Space Science Institute BoulderCO80301 United States Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Materials Science and Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Deloitte Consulting New YorkNY10112 United States Computer Science Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Microsoft RedmondWA98052 United States Physics and Astronomy Department University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Raybeam Inc. Mountain ViewCA94041 United States SpaceX HawthorneCA90250 United States Reliable Robotics Corporation Mountain ViewCA94043 United States Los Alamos National Laboratory Los AlamosNM87545 United States Mathematics Department University of California Los AngelesCA90095 United States Planet Labs PBC San FranciscoCA94107 United States KSAT Inc. De

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data from the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17s, or ∆L ∼ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using 2 years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 − 7 at dusk, while a smaller subset exists at L ∼ 8 − 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive

关键词： Radiation belts

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Interactive control approach to 3D shape reconstruction

arXiv

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arXiv 2019年

作者： Islam, Bipul Liu, Ji Yezzi, Anthony Sandhu, Romeil Department of Computer Science & Biomedical Informatics Stony Brook University Stony BrookNY11794 United States Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY11794 United States Department of Electrical and Computer Engineering Georgia Tech AtlantaGA30309 United States Departments of Applied Mathematics & Statistics Stony Brook University Stony BrookNY11794 United States

The ability to accurately reconstruct the 3D facets of a scene is one of the key problems in robotic vision. However, even with recent advances with machine learning, there is no high-fidelity universal 3D reconstruction method for this optimization problem as schemes often cater to specific image modalities and are often biased by scene abnormalities. Simply put, there always remains an "information" gap due to the dynamic nature of real-world scenarios. To this end, we demonstrate a feedback control framework which invokes operator inputs (also prone to errors) in order to augment existing reconstruction schemes. For proof-of-concept, we choose a classical region-based stereoscopic reconstruction approach and show how an ill-posed model can be augmented with operator input to be much more robust to scene artifacts. We provide necessary conditions for stability via Lyapunov analysis and perhaps more importantly, we show that the stability depends on a notion of absolute curvature. Mathematically, this aligns with previous work that has shown Ricci curvature as proxy for functional robustness of dynamical networked systems. We conclude with results that show how our method can improve standalone reconstruction schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Stereo image processing

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Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes

arXiv

引用

arXiv 2020年

作者： She, Xichen Zhai, Yaya Henao, Ricardo Woods, Christopher W. Chiu, Christopher Ginsburg, Geoffrey S. Song, Peter X.K. Hero, Alfred O. Department of Biostatistics University of Michigan Ann ArborMI48109 United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI48109 United States Center for Applied Genomics and Precision Medicine Duke University DurhamNC27708 United States Department of Infectious Disease Imperial College London LondonW2 1PG United Kingdom Departments of Electrical Engineering and Computer Science Biomedical Engineering and Statistics University of Michigan Ann ArborMI48109 United States

Objective: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. Methods: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher’s linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring a priori information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to a H1N1 influenza pathogen. Results: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. Conclusion: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. Significance: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Wearable sensors

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Discrimination of carbonatite and Mississippi Valley-type deposits by partial least squares-discriminant analysis of trace elements and Mg isotope compositions in dolomite

引用

Journal of Geochemical Exploration 2025年 278卷

作者： Araoka, Daisuke Itano, Keita Simandl, George J. Paradis, Suzanne Yoshimura, Toshihiro Ibaraki Tsukuba305-8567 Japan Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita University Akita010-8502 Japan Department of Mineralogy and Petrology University of Granada Avenida de Fuente Nueva Granada 18071 Spain School of Earth and Ocean Sciences University of Victoria VictoriaBCV8P 3E6 Canada Mineralium Research & Advisory VictoriaBCV8X 3H9 Canada Geological Survey of Canada Natural Resources Canada SidneyBCV8L 4B2 Canada Kanagawa Yokosuka237-0061 Japan

Carbonatite-related rare earth element (REE), Nb, fluorite, and phosphate deposits and Mississippi Valley-type (MVT) deposits containing Zn, Pb, Ag, and Ge are increasingly important sources of critical metals and minerals. Geochemical classification of carbonatite-related and MVT deposits using chemical compositions of dolomite, the most common gangue mineral in such deposits, is potentially useful for tracing the deposit types and assisting in mineral exploration. Here, we studied variations in elemental contents (42 elements) and δ26Mg of dolomite from deposits in the eastern Canadian Cordillera, where carbonatite-related and MVT deposits coexist, to examine its usefulness for geochemical discrimination. Our dataset showed systematic differences between carbonatite and MVT deposits: dolomites from carbonatites were enriched in Fe, Ga, Sr, As, Ba, REEs, and Th and had high δ26Mg values, whereas dolomites from MVT deposits were enriched in Zn and Pb, but there were obvious overlaps. We developed an accurate discrimination model by performing a partial least squares regression-discriminant analysis (PLS-DA) that considered all variables simultaneously. The results of PLS-DA showed that two components were sufficient for accurate classification of dolomites from the two deposit types in the study area in comparison with the results of principal component analysis and liner discriminant analysis (PCA-LDA). The δ26Mg, Sr/Mg, Zn/Mg, Ba/Mg, and Pb/Mg yielded the highest variable importance on projection (VIP) scores, which is consistent with the geochemical parameters expected from previous studies on carbonatites and MVT deposits. Furthermore, we applied the developed discrimination model to a dataset of dolomite from other deposit (Rock Canyon Creek deposit) and unmineralized dolomite from the host rocks. The dolomites from the Rock Canyon Creek deposit were classified as carbonatite-related deposits, consistent with recent research, although their elemental signatures

关键词： Dolomite Partial least squares-discriminant analysis Carbonatite Mississippi Valley-type deposit Mg isotope Trace elements

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Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

arXiv

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arXiv 2021年

作者： Tennant, Daniel M. Dai, Xi Martinez, Antonio J. Trappen, Robbyn Melanson, Denis Yurtalan, M.A. Tang, Yongchao Bedkihal, Salil Yang, Rui Novikov, Sergei Grover, Jeffery A. Disseler, Steven M. Basham, James I. Das, Rabindra Kim, David K. Melville, Alexander J. Niedzielski, Bethany M. Weber, Steven J. Yoder, Jonilyn L. Kerman, Andrew J. Mozgunov, Evgeny Lidar, Daniel A. Lupascu, Adrian Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Northrop Grumman Corporation LinthicumMD21090 United States MIT Lincoln Laboratory LexingtonMA02421 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Departments of Electrical & Computer Engineering Chemistry and Physics Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada

Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits. © 2021, CC BY.

关键词： Spin dynamics

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Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

arXiv

引用

arXiv 2019年

作者： Wang, Minjie Allen, Genevera I. Department of Statistics Rice University HoustonTX United States Departments of Electrical and Computer Engineering Statistics and Computer Science Rice University HoustonTX United States Jan and Dan Duncan Neurological Research Institute Baylor College of Medicine HoustonTX United States

In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data-view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that will inherit the strong statistical, mathematical and empirical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data-view that are best for determining the groups, often leading to improved integrative clustering. To fit our model, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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GPAW: An open Python package for electronic-structure calculations

arXiv

引用

arXiv 2023年

作者： Mortensen, Jens Jørgen Larsen, Ask Hjorth Kuisma, Mikael Ivanov, Aleksei V. Taghizadeh, Alireza Peterson, Andrew Haldar, Anubhab Dohn, Asmus Ougaard Schäfer, Christian Jónsson, Elvar Örn Hermes, Eric D. Nilsson, Fredrik Andreas Kastlunger, Georg Levi, Gianluca Jónsson, Hannes Häkkinen, Hannu Fojt, Jakub Kangsabanik, Jiban Sødequist, Joachim Lehtomäki, Jouko Heske, Julian Enkovaara, Jussi Winther, Kirsten Trøstrup Dulak, Marcin Melander, Marko M. Ovesen, Martin Louhivuori, Martti Walter, Michael Gjerding, Morten Lopez-Acevedo, Olga Erhart, Paul Warmbier, Robert Würdemann, Rolf Kaappa, Sami Latini, Simone Boland, Tara Maria Bligaard, Thomas Skovhus, Thorbjørn Susi, Toma Maxson, Tristan Rossi, Tuomas Chen, Xi Schmerwitz, Yorick Leonard A. Schiøtz, Jakob Olsen, Thomas Jacobsen, Karsten Wedel Thygesen, Kristian Sommer CAMd Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Riverlane Ltd St Andrews House 59 St Andrews Street CambridgeCB2 3BZ United Kingdom School of Engineering Brown University ProvidenceRI United States Department of Electrical and Computer Engineering Boston University United States Department of Physics Technical University of Denmark Lyngby2800 Denmark Science Institute Faculty of Physical Sciences VR-III University of Iceland Reykjavík107 Iceland Department of Physics Chalmers University of Technology GothenburgSE-412 96 Sweden Science Institute Faculty of Physical Sciences University of Iceland VR-III Reykjavík107 Iceland Quantum-Si 29 Business Park Drive BranfordCT06405 United States CatTheory Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Departments of Physics and Chemistry Nanoscience Center University of Jyväskylä JyväskyläFI-40014 Finland Department of Applied Physics Aalto University P.O. Box 11100 Aalto00076 Finland CSC – IT Center for Science Ltd. P.O. Box 405 EspooFI-02101 Finland SUNCAT Center for Interface Science and Catalysis SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Chemistry Nanoscience Center University of Jyväskylä JyväskyläFI-40014 Finland University of Freiburg FIT Freiburg Centre for Interactive Materials and Bioinspired Technologies Georges-Köhler-Allee 105 Freiburg79110 Germany Biophysics of Tropical Diseases Max Planck Tandem Group University of Antioquia UdeA Medellin050010 Colombia School of Physics Mandelstam Institute for Theoretical Physics University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg2001 South Africa Freiburger Materialforschungszentrum Universität Freiburg Stefan-Meier-Straße 21 FreiburgD-79104 Germany Computational Physics Laboratory Tampere University P.O. Box 692 TampereFI-33014 Finland Nanomade Department of Physics Technical University of Denmark Kgs Lyngb

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE) providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe–Salpeter Equation (BSE), variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn–Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support of GPU acceleration has been achieved with minor modifications of the GPAW code thanks to the CuPy library. We end the review with an outlook describing some future plans for GPAW. © 2023, CC BY-SA.

关键词： Python

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2D Compass Codes

引用

Physical Review X 2019年第2期9卷 021041-021041页

作者： Muyuan Li Daniel Miller Michael Newman Yukai Wu Kenneth R. Brown School of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA Institut für Theoretische Physik III Heinrich-Heine-Universität Düsseldorf D-40225 Düsseldorf Germany Departments of Electrical and Computer Engineering Chemistry and Physics Duke University Durham North Carolina 27708 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA

The compass model on a square lattice provides a natural template for building subsystem stabilizer codes. The surface code and the Bacon-Shor code represent two extremes of possible codes depending on how many gauge qubits are fixed. We explore threshold behavior in this broad class of local codes by trading locality for asymmetry and gauge degrees of freedom for stabilizer syndrome information. We analyze these codes with asymmetric and spatially inhomogeneous Pauli noise in the code capacity and phenomenological models. In these idealized settings, we observe considerably higher thresholds against asymmetric noise. At the circuit level, these codes inherit the bare-ancilla fault tolerance of the Bacon-Shor code.

关键词： Quantum error correction Quantum memories Surface code quantum computing

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

引用

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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A Gd@C82-based single molecular electret device

arXiv

引用

arXiv 2020年

作者： Zhang, Kangkang Wang, Cong Zhang, Minhao Bai, Zhanbin Xie, Fangfang Tan, Yuanzhi Guo, Yilv Hu, Kuo-Juei Cao, Lu Zhang, Shuai Tu, Xuecou Kang, Lin Chen, Jian Wu, Peiheng Wang, Xuefeng Wang, Jinlan Liu, Junming Wang, Baigeng Wang, Guanghou Xie, Suyuan Ji, Wei Shi, Su-Fei Reed, M.A. Song, Fengqi National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructures School of Physics Nanjing University Nanjing210093 China Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-Nano Devices Department of Physics Renmin University of China Beijing100872 China State Key Laboratory of Physical Chemistry of Solid Surfaces Department of Chemistry College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China School of Physics Southeast University Nanjing211189 China School of Electronic Science and Engineering and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210023 China Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Applied Physics and Electrical Engineering Yale University New HavenCT06520 United States Nanjing211805 China

Single molecular electrets exhibiting single‐molecule electric polarization switching have been long desired as a platform for extremely small non-volatile storage devices, although it is controversial because of the poor stability of single molecular electric dipoles. Here we study the single molecular device of Gd@C82, where the encapsulated Gd atom forms a charge center, and we have observed a gate-controlled switching behavior between two sets of single-electron-transport stability diagrams. The switching is operated in a hysteresis loop with a coercive gate field of around 0.5 V/nm. Theoretical calculations have assigned the two conductance diagrams to corresponding energy levels of two states that the Gd atom is trapped at two different sites of the C82 cage, which possess two different permanent electrical dipole orientations. The two dipole states are stabilized by the anisotropic energy and separated by a transition energy barrier of 70 meV. Such switching is then accessed to the electric field driven reorientation of individual dipole while overcoming the barriers by the coercive gate field, and demonstrates the creation of a single molecular electret. Copyright © 2020, The Authors. All rights reserved.

关键词： Switching

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