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Type-II Dirac cones and electron-phonon interaction in monolayer biphenylene from first-principles calculations

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Physical Review B 2021年第23期104卷 235422-235422页

作者： Peng-Fei Liu Jingyu Li Chi Zhang Xin-Hai Tu Junrong Zhang Ping Zhang Bao-Tian Wang David J. Singh Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei 230031 China Foshan (Southern China) Institute for New Materials Foshan 528200 Guangdong China College of Electrical Engineering Henan University of Technology Zhengzhou 450001 China School of Physics and Physical Engineering Qufu Normal University Qufu 273165 China LCP Institute of Applied Physics and Computational Mathematics Beijing 100088 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Department of Physics and Astronomy University of Missouri Columbia Missouri 65211 USA Department of Chemistry University of Missouri Columbia Missouri 65211 USA

We report a first-principles investigation of electronic structure, topological bands, and electron-phonon interactions in metallic biphenylene sheets. Biphenylene is a recently synthesized sp2-bonded carbon allotrope. We find coupling of electrons at the Fermi surface to very high frequency carbon-derived phonons, analogous to superconducting MgB2. This leads to low-temperature weak coupling superconductivity due to an unusual combination of exceptionally large logarithmically averaged phonon frequency ωlog=1369 K and moderate electron-phonon coupling. The electronic structure shows a two-band Fermi surface dominated by C pz orbitals and a pair of type-II tilted Dirac cones along the Γ-Y line at the Brillouin zone boundary. Berry curvature and edge-state calculations show that monolayer biphenylene is a two-dimensional Z2 topological material. Thus, monolayer biphenylene is predicted to be a topological superconductor based on C p orbitals and high-frequency phonons.

关键词： Edge states Electron-phonon coupling First-principles calculations Superconductors Topological materials 2-dimensional systems Dirac semimetal BCS theory Density functional theory

Robust convergence analysis of three-operator splitting

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arXiv 2019年

作者： Wang, Han Fazlyab, Mahyar Chen, Shaoru Preciado, Victor M. Department of Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States

Operator splitting methods solve composite optimization problems by breaking them into smaller sub-problems that can be solved sequentially or in parallel. In this paper, we propose a unified framework for certifying both linear and sublinear convergence rates for three-operator splitting (TOS) method under a variety of assumptions about the objective function. By viewing the algorithm as a dynamical system with feedback uncertainty (the oracle model), we leverage robust control theory to analyze the worst-case performance of the algorithm using matrix inequalities. We then show how these matrix inequalities can be used to verify sublinear/linear convergence of the TOS algorithm and guide the search for selecting the parameters of the algorithm (both symbolically and numerically) for optimal worst-case performance. We illustrate our results numerically by solving an input-constrained optimal control problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamical systems

Robust model predictive control via system level synthesis

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arXiv 2019年

作者： Wang, Han Chen, Shaoru Preciado, Victor M. Matni, Nikolai Department of Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States

In this paper, we consider the robust model predictive control (MPC) of a linear time-varying (LTV) system with norm bounded disturbances and model uncertainty, wherein a series of constrained optimal control problems (OCPs) are solved. Guaranteeing robust feasibility of these OCPs is challenging, due to both disturbances perturbing the predicted states, and model uncertainty which can render the closed-loop system unstable. As such, a trade-off between the numerical tractability and conservativeness of the solutions is often required. We use the System Level Synthesis (SLS) framework to reformulate these constrained OCPs over closed-loop system responses, and show that this allows us to transparently account for norm bounded additive disturbances and LTV model uncertainty by computing robust state feedback policies. We further show that by exploiting the underlying linear-fractional structure of the resulting robust OCPs, we can significantly reduce the conservatism of existing SLS-based robust control methods. We conclude with numerical examples demonstrating the effectiveness of our methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Uncertainty analysis

Dynamics of Nanosecond Laser Pulse Propagation and of Associated Instabilities in a Magnetized Underdense Plasma

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Physical Review Letters 2023年第26期130卷 265101-265101页

作者： W. Yao A. Higginson J.-R Marquès P. Antici J. Béard K. Burdonov M. Borghesi A. Castan A. Ciardi B. Coleman S. N. Chen E. d’Humières T. Gangolf L. Gremillet B. Khiar L. Lancia P. Loiseau X. Ribeyre A. Soloviev M. Starodubtsev Q. Wang J. Fuchs LULI—CNRS CEA UPMC Univ Paris 06: Sorbonne Université École Polytechnique Institut Polytechnique de Paris—F-91128 Palaiseau cedex France Sorbonne Université Observatoire de Paris Université PSL CNRS LERMA F-75005 Paris France Center for Energy Research University of California San Diego 9500 Gilman Drive La Jolla California 92093-0417 USA INRS-EMT 1650 boul Lionel-Boulet Varennes QC J3X 1S2 Canada CNRS LNCMI Univ Toulouse 3 INSA Toulouse Univ Grenoble Alpes EMFL 31400 Toulouse France JIHT Russian Academy of Sciences 125412 Moscow Russia School of Mathematics and Physics The Queen’s University Belfast Belfast United Kingdom CEA DAM DIF F-91297 Arpajon France “Horia Hulubei” National Institute for Physics and Nuclear Engineering RO-077125 Bucharest-Magurele Romania University of Bordeaux CELIA CNRS CEA UMR 5107 F-33405 Talence France Université Paris-Saclay CEA LMCE 91680 Bruyères-le-Châtel France Office National d’Etudes et de Recherches Aérospatiales (ONERA) Palaiseau 91123 France IAP-RAS Nizhny Novgorod 603950 Russia Institute of Applied Physics and Computational Mathematics Beijing 100094 China Department of Electrical and Computer Engineering University of Alberta 9211 116 St. NW Edmonton Alberta T6G 1H9 Canada

The propagation and energy coupling of intense laser beams in plasmas are critical issues in inertial confinement fusion. Applying magnetic fields to such a setup has been shown to enhance fuel confinement and heating. Here we report on experimental measurements demonstrating improved transmission and increased smoothing of a high-power laser beam propagating in a magnetized underdense plasma. We also measure enhanced backscattering, which our kinetic simulations show is due to magnetic confinement of hot electrons, thus leading to reduced target preheating.

关键词： High-energy-density plasmas Indirect drive Laser light absorption in plasmas Laser-plasma interactions Plasma stability

Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

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NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

Training of Physical Neural Networks

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

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Research Square

Research Square 2021年

作者： Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome. © 2021, CC BY.

关键词： Coronavirus

Dynamic Mode Decomposition and Sparse Measurements for Characterization and Monitoring of Power System Disturbances

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arXiv 2019年

作者： Ramos, J. Jorge Kutz, J. Nathan Graduate Program in Electrical Engineering Cinvestav Unidad Guadalajara Guadalajaram Jal Mexico Department of Applied Mathematics University of Washington SeattleWA98195 United States

We introduce the dynamics mode decomposition for monitoring wide-area power grid networks from sparse measurement data. The mathematical framework fuses data from multiple sensors based on multivariate statistics, providing accurate full state estimation from limited measurements and generating data-driven forecasts for the state of the system. Our proposed data-driven strategy, which is based on energy metrics, can be used for the analysis of major disturbances in the network. The approach is tested and validated using time domain simulations in the IEEE 118 bus system under various disturbance scenarios and under different sparse observations of the system. In addition to state reconstruction, the minimal number of sensors required for monitoring disturbances can be evaluated. Visualization techniques are developed in order to aid in the analysis and characterization of the system after disturbance. Copyright © 2019, The Authors. All rights reserved.

关键词： Time domain analysis

Unsupervised particle sorting for high-resolution single-particle cryo-EM

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arXiv 2019年

作者： Zhou, Ye Moscovich, Amit Bendory, Tamir Bartesaghi, Alberto Department of Computer Science Duke University Durham United States Program in Applied and Computational Mathematics Princeton University United States School of Electrical Engineering Tel Aviv University Tel Aviv Israel Department of Biochemistry Duke University School of Medicine Durham United States Department of Electrical and Computer Engineering Duke University Durham United States

Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to successfully execute the structure determination workflow. Many critical data processing steps, however, still require expert user intervention in order to converge to the correct high-resolution structure. In particular, strategies to identify homogeneous populations of particles rely heavily on subjective criteria that are not always consistent or reproducible among different users. Here, we explore the use of unsupervised strategies for particle sorting that are compatible with the autonomous operation of the image processing pipeline. More specifically, we show that particles can be successfully sorted based on a simple statistical model for the distribution of scores assigned during refinement. This represents an important step towards the development of automated workflows for protein structure determination using single-particle *** Codes 62P10 Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems