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Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics

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arXiv 2018年

作者： Deringer, Volker L. Bernstein, Noam Bartók, Albert P. Cliffe, Matthew J. Kerber, Rachel N. Marbella, Lauren E. Grey, Clare P. Elliott, Stephen R. Csányi, Gábor Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Center for Materials Physics and Technology U.S. Naval Research Laboratory WashingtonDC20375 United States Scientific Computing Department Science and Technology Facilities Council Rutherford Appleton Laboratory OxfordshireOX11 0QX United Kingdom

Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained by harnessing the power of machine-learning algorithms to create interatomic potentials. Our best a-Si network is obtained by cooling from the melt in molecular-dynamics simulations, at a rate of 1011 K/s (that is, on the 10 ns timescale). This structure shows a defect concentration of below 2% and agrees with experiments regarding excess energies, diffraction data, as well as 29Si solid-state NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4,096-atom system which correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning interatomic potentials for elucidating accurate structures and properties of amorphous functional materials. Copyright © 2018, The Authors. All rights reserved.

关键词： Amorphous silicon

The CECAM Electronic Structure Library and the modular software development paradigm

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arXiv 2020年

作者： Oliveira, Micael J. T. Papior, Nick Pouillon, Yann Blum, Volker Artacho, Emilio Caliste, Damien Corsetti, Fabiano Gironcoli, Stefanode Elena, Alin M. Garca, Alberto Garca-Suarez, Vctor M. Genovese, Luigi Huhn, William P. Huhs, Georg Kokott, Sebastian Kucukbenli, Emine Larsen, Ask H. Lazzaro, Alo Lebedeva, Irina V. Li, Yingzhou Lopez-Duran, David Lopez-Tarifa, Pablo Luders, Martin Marques, Miguel A. L. Minar, Jan Mohr, Stephan Mosto, Arash A. O'Cais, Alan Payne, Mike C. Ruh, Thomas Smith, Daniel G. A. Soler, Jose M. Strubbe, David A. Tancogne-Dejean, Nicolas Tildesley, Dominic Torrent, Marc Wen-Zhe, Victor Max Planck Institute for the Structure and Dynamics of Matter HamburgD-22761 Germany DTU Computing Center Technical University of Denmark Lyngby2800 Denmark Departamento CITIMAC Universidad de Cantabria Santander Spain Simune Atomistics San Sebastian20018 Spain Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Department of Chemistry Duke University DurhamNC27708 United States CIC Nanogune BRTA DIPC San Sebastian20018 Spain Ikerbasque Basque Foundation for Science Bilbao48011 Spain Theory of Condensed Matter Cavendish Laboratory University of Cambridge CambridgeCB30HE United Kingdom Department of Physics IRIG Univ. Grenoble Alpes CEA GrenobleF-38000 France Departments of Materials and Physics Thomas Young Centre for Theory and Simulation of Materials Imperial College London LondonSW72AZ United Kingdom Synopsys Denmark Copenhagen2100 Denmark Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Scientific Computing Department Daresbury Laboratory WarringtonWA44AD United Kingdom Institut de Ciencia de Materials de Barcelona BellaterraE-08193 Spain Departamento de Fisica Universidad de Oviedo CINN Oviedo33007 Spain Barcelona Supercomputing Center Barcelona08034 Spain Fritz Haber Institut Berlin14195 Germany John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Nano-Bio Spectroscopy Group ETSF Departamento de Fisica de Materiales Universidad Del Pais Vasco San Sebastian20018 Spain Department of Chemistry University of Zurich ZurichCH-8057 Switzerland CIC Nanogune BRTA San Sebastian20018 Spain Department of Mathematics Duke University DurhamNC27708-0320 United States Centro de Fisica de Materiales Centro Mixto CSIC-UPV San Sebastian20018 Spain Institut fur Physik Martin-Luther-Universitat Halle-Wittenberg Halle Saale06120 Germany New Technologies Research Centre University of West Bohemia Plzen3

First-principles electronic structure calculations are now accessible to a very large community of users across many disciplines thanks to many successful software packages, some of which are described in this special issue. The traditional coding paradigm for such packages is monolithic, i.e., regardless of how modular its internal structure may be, the code is built independently from others, essentially from the compiler up, possibly with the exception of linear-algebra and message-passing libraries. This model has endured and been quite successful for decades. The successful evolution of the electronic structure methodology itself, however, has resulted in an increasing complexity and an ever longer list of features expected within all software packages, which implies a growing amount of replication between different packages, not only in the initial coding but, more importantly, every time a code needs to be re-engineered to adapt to the evolution of computer hardware architecture. The Electronic Structure Library (ESL) was initiated by CECAM (the European Centre for Atomic and Molecular Calculations) to catalyze a paradigm shift away from the monolithic model and promote modularization, with the ambition to extract common tasks from electronic structure codes and redesign them as open-source libraries available to everybody. Such libraries include, e.g., \heavy-duty"ones that have the potential for a high degree of parallelisation and adaptation to novel hardware within them, thereby separating the sophisticated computer science aspects of performance optimization and reengineering from the computational science done by, e.g., physicists and chemists when implementing new ideas. We envisage that this modular paradigm will improve overall coding eficiency and enable specialists (whether they be computer scientists or computational scientists) to use their skills more effectively, and will lead to a more dynamic evolution of software in the community as well as

关键词： Libraries

A morphology-independent data analysis method for detecting and characterizing gravitational wave echoes

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arXiv 2018年

作者： Tsang, Ka Wa Rollier, Michiel Ghosh, Archisman Samajdar, Anuradha Agathos, Michalis Chatziioannou, Katerina Cardoso, Vitor Khanna, Gaurav Van Den Broeck, Chris Nikhef - National Institute for Subatomic Physics 105 Science Park Amsterdam1098 XG Netherlands DAMTP Centre for Mathematical Sciences University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Canadian Institute for Theoretical Astrophysics University of Toronto 60 St. George Street TorontoONM5S 3H8 Canada CENTRA Departamento de Fisica Instituto Superior Técnico - IST Universidade de Lisboa - UL Avenida Rovisco Pais 1 Lisboa1049 Portugal Department of Physics and Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth North DartmouthMA02747 United States Van Swinderen Institute for Particle Physics and Gravity University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands

The ability to directly detect gravitational waves has enabled us to empirically probe the nature of ultracompact relativistic objects. Several alternatives to the black holes of classical general relativity have been proposed which do not have a horizon, in which case a newly formed object (e.g. as a result of binary merger) may emit echoes: Bursts of gravitational radiation with varying amplitude and duration, but arriving at regular time intervals. Unlike in previous template-based approaches, we present a morphology-independent search method to find echoes in the data from gravitational wave detectors, based on a decomposition of the signal in terms of generalized wavelets consisting of multiple sine-Gaussians. The ability of the method to discriminate between echoes and instrumental noise is assessed by inserting into the noise two di_erent signals: A train of sine-Gaussians, and an echoing signal from an extreme mass-ratio inspiral of a particle into a Schwarzschild vacuum spacetime, with reflective boundary conditions close to the horizon. We find that both types of signals are detectable for plausible signal-to-noise ratios in existing detectors and their near-future upgrades. Finally, we show how the algorithm can provide a characterization of the echoes in terms of the time between successive bursts, and damping and widening from one echo to the next. Copyright © 2018, The Authors. All rights reserved.

关键词： Morphology

Interplay of coherent and dissipative dynamics in condensates of light

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arXiv 2017年

作者： Radonjić, Milan Kopylov, Wassilij Balaž, Antun Pelster, Axel Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Serbia Faculty of Physics University of Vienna Austria Physics Department and Research Center OPTIMAS Technische Universität Kaiserslautern Germany Institute for Theoretical Physics Technische Universität Berlin Germany

Based on the Lindblad master equation approach we obtain a detailed microscopic model of photons in a dye-filled cavity, which features condensation of light. To this end we generalise a recent non-equilibrium approach of Kirton and Keeling such that the dye-mediated contribution to the photon-photon interaction in the light condensate is accessible due to an interplay of coherent and dissipative dynamics. We describe the steady-state properties of the system by analysing the resulting equations of motion of both photonic and matter degrees of freedom. In particular, we discuss the existence of two limiting cases for steady states: photon Bose-Einstein condensate and laser-like. In the former case, we determine the corresponding dimensionless photon-photon interaction strength by relying on realistic experimental data and find a good agreement with previous theoretical estimates. Furthermore, we investigate how the dimensionless interaction strength depends on the respective system parameters. Copyright © 2017, The Authors. All rights reserved.

关键词： Bose-Einstein condensation

Classification of crystallization outcomes using deep convolutional neural networks

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arXiv 2018年

作者： Bruno, Andrew E. Charbonneau, Patrick Newman, Janet Snell, Edward H. So, David R. Vanhoucke, Vincent Watkins, Christopher J. Williams, Shawn Wilson, Julie Center for Computational Research University at Buffalo BuffaloNY United States Department of Chemistry Duke University DurhamNC United States Department of Physics Duke University DurhamNC United States Collaborative Crystallisation Centre CSIRO ParkvilleVIC Australia Hauptman-Woodward Medical Research Institute and SUNY Buffalo Department of Materials Design and Innovation BuffaloNY14203 United States Google Brain Google Inc. Mountain ViewCA United States IMandT Scientific Computing CSIRO Clayton SouthVIC Australia Platform Technology and Sciences GlaxoSmithKline Inc. CollegevillePA United States Department of Mathematics University of York York United Kingdom

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications. Author summary: Protein crystal growth experiments are routinely imaged, but the mass of accumulated data is difficult to manage and analyze. Using state-of-the-art machine learning algorithms on a large and diverse set of reference images, we manage to recapitulate the labels of a remarkably large fraction of the set. This automation should enable a number of industrial and fundamental applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

Heavy physics Contributions to Neutrinoless Double Beta Decay from QCD

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Physical Review Letters 2018年第17期121卷 172501-172501页

作者： A. Nicholson E. Berkowitz H. Monge-Camacho D. Brantley N. Garron C. C. Chang E. Rinaldi M. A. Clark B. Joó T. Kurth B. C. Tiburzi P. Vranas A. Walker-Loud Department of Physics and Astronomy University of North Carolina Chapel Hill North Carolina 27516-3255 USA Department of Physics University of California Berkeley California 94720 USA Institut für Kernphysik and Institute for Advanced Simulation Forschungszentrum Jülich 54245 Jülich Germany Department of Physics The College of William & Mary Williamsburg Virginia 23187 USA Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Physics Division Lawrence Livermore National Laboratory Livermore California 94550 USA Theoretical Physics Division Department of Mathematical Sciences University of Liverpool Liverpool L69 3BX United Kingdom Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan RIKEN-BNL Research Center Brookhaven National Laboratory Upton New York 11973 USA NVIDIA Corporation 2701 San Tomas Expressway Santa Clara California 95050 USA Scientific Computing Group Thomas Jefferson National Accelerator Facility Newport News Virginia 23606 USA NERSC Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics The City College of New York New York New York 10031 USA Graduate School and University Center The City University of New York New York New York 10016 USA

Observation of neutrinoless double beta decay, a lepton number violating process that has been proposed to clarify the nature of neutrino masses, has spawned an enormous world-wide experimental effort. Relating nuclear decay rates to high-energy, beyond the standard model (BSM) physics requires detailed knowledge of nonperturbative QCD effects. Using lattice QCD, we compute the necessary matrix elements of short-range operators, which arise due to heavy BSM mediators, that contribute to this decay via the leading order π−→π+ exchange diagrams. Utilizing our result and taking advantage of effective field theory methods will allow for model-independent calculations of the relevant two-nucleon decay, which may then be used as input for nuclear many-body calculations of the relevant experimental decays. Contributions from short-range operators may prove to be equally important to, or even more important than, those from long-range Majorana neutrino exchange.

关键词： Lattice QCD Lattice field theory Neutrinoless double beta decay

Black hole spectroscopy: Systematic errors and ringdown energy estimates

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arXiv 2017年

作者： Baibhav, Vishal Berti, Emanuele Cardoso, Vitor Khanna, Gaurav Department of Physics and Astronomy University of Mississippi UniversityMS38677 United States CENTRA Departamento de Física Instituto Superior Técnico Universidade de Lisboa Avenida Rovisco Pais 1 Lisboa1049 Portugal Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Physics & Center for Scientific Computing and Visualization Research University of Massachusetts Dartmouth North DartmouthMA02747 United States

The relaxation of a distorted black hole to its final state provides important tests of general relativity within the reach of current and upcoming gravitational wave facilities. In black hole perturbation theory, this phase consists of a simple linear superposition of exponentially damped sinusoids (the quasinormal modes) and of a power-law tail. How many quasinormal modes are necessary to describe waveforms with a prescribed precision? What error do we incur by only including quasinormal modes, and not tails? What other systematic effects are present in current state-of-the-art numerical waveforms? These issues, which are basic to testing fundamental physics with distorted black holes, have hardly been addressed in the literature. We use numerical relativity waveforms and accurate evolutions within black hole perturbation theory to provide some answers. We show that (i) a determination of the fundamental l = m = 2 quasinormal mode to within 1% or better requires the inclusion of at least the first overtone, and preferably of the first two or three overtones;(ii) a determination of the black hole mass and spin with precision better than 1% requires the inclusion of at least two quasinormal modes for any given angular harmonic mode (`, m). We also improve on previous estimates and fits for the ringdown energy radiated in the various multipoles. These results are important to quantify theoretical (as opposed to instrumental) limits in parameter estimation accuracy and tests of general relativity allowed by ringdown measurements with high signal-to-noise ratio gravitational wave detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Relativity

A pulsar-based timescale from the International Pulsar Timing Array

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arXiv 2019年

作者： Hobbs, G. Guo, L. Caballero, R.N. Coles, W. Lee, K.J. Manchester, R.N. Reardon, D.J. Matsakis, D. Tong, M.L. Arzoumanian, Z. Bailes, M. Bassa, C.G. Bhat, N.D.R. Brazier, A. Burke-Spolaor, S. Champion, D.J. Chatterjee, S. Cognard, I. Dai, S. Desvignes, G. Dolch, T. Ferdman, R.D. Graikou, E. Guillemot, L. Janssen, G.H. Keith, M.J. Kerr, M. Kramer, M. Lam, M.T. Liu, K. Lyne, A. Lazio, T.J.W. Lynch, R. McKee, J.W. McLaughlin, M.A. Mingarelli, C.M.F. Nice, D.J. Oslowski, S. Pennucci, T.T. Perera, B.B.P. Perrodin, D. Possenti, A. Russell, C.J. Sanidas, S. Sesana, A. Shaifullah, G. Shannon, R.M. Simon, J. Spiewak, R. Stairs, I.H. Stappers, B.W. Swiggum, J.K. Taylor, S.R. Theureau, G. Toomey, L. van Haasteren, R. Wang, J.B. Wang, Y. Zhu, X.J. U.S. Naval Observatory WashingtonD.C. United States CSIRO Astronomy and Space Science Australia Telescope National Facility Box 76 EppingNSW1710 Australia Shanghai Astronomical Observatory CAS Shanghai200030 China Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Kavli institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California at San Diego La JollaCA92093 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology PO Box 218 HawthornVIC3122 Australia National Time Service Center CAS Xi’anShaanxi710600 China Astrophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Cornell Center for Advanced Computing Cornell University IthacaNY14853 United States Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Laboratoire de Physique et Chimie de l’Environnement et de l’Espace LPC2E CNRS- Université d’Orléans OrléansF-45071 France Station de radioastronomie de Nançay Observatoire de Paris PSL University CNRS/INSU NancayF-18330 France Department of Physics Hillsdale College 33 E. College Street HillsdaleMI49242 United States University of East Anglia Norwich Research Park NorwichNR4 7TJ United Kingdom Department of Astrophysics IMAPP Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Jodrell Bank Centre for Astrophysics University of Manchester ManchesterM13 9PL Unite

We have constructed a new timescale, TT(IPTA16), based on observations of radio pulsars presented in the first data release from the International Pulsar Timing Array (IPTA). We used two analysis techniques with independent estimates of the noise models for the pulsar observations and different algorithms for obtaining the pulsar timescale. The two analyses agree within the estimated uncertainties and both agree with TT(BIPM17), a post-corrected timescale produced by the Bureau International des Poids et Mesures (BIPM). We show that both methods could detect significant errors in TT(BIPM17) if they were present. We estimate the stability of the atomic clocks from which TT(BIPM17) is derived using observations of four rubidium fountain clocks at the US Naval Observatory. Comparing the power spectrum of TT(IPTA16) with that of these fountain clocks suggests that pulsar-based timescales are unlikely to contribute to the stability of the best timescales over the next decade, but they will remain a valuable independent check on atomic timescales. We also find that the stability of the pulsar-based timescale is likely to be limited by our knowledge of solar-system dynamics, and that errors in TT(BIPM17) will not be a limiting factor for the primary goal of the IPTA, which is to search for the signatures of nano-Hertz gravitational waves. Copyright © 2019, The Authors. All rights reserved.

关键词： Pulsars

Machine learning a general purpose interatomic potential for silicon

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arXiv 2018年

作者： Bartók, Albert P. Kermode, James Bernstein, Noam Csányi, Gábor Scientific Computing Department Rutherford Appleton Laboratory Science and Techology Facilities Council DidcotOX11 0QX United Kingdom Warwick Centre for Predictive Modelling School of Engineering University of Warwick CoventryCV4 7AL United Kingdom U. S. Naval Research Laboratory Center for Materials Physics and Technology Washington DC20375 United States Engineering Laboratory University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom

The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length and time scales due to computational cost and its scaling. Techniques based on machine learning ideas for interpolating the Born-Oppenheimer potential energy surface without explicitly describing electrons have recently shown great promise, but accurately and efficiently fitting the physically relevant space of configurations has remained a challenging goal. Here we present a Gaussian Approximation Potential for silicon that achieves this milestone, accurately reproducing density functional theory reference results for a wide range of observable properties, including crystal, liquid, and amorphous bulk phases, as well as point, line, and plane defects. We demonstrate that this new potential enables calculations that would be very expensive with a first principles electronic structure method, such as finite temperature phase boundary lines, self-diffusivity in the liquid, formation of the amorphous by slow quench, and dynamic brittle fracture. We show that the uncertainty quantification inherent to the Gaussian process regression framework gives a qualitative estimate of the potential's accuracy for a given atomic configuration. The success of this model shows that it is indeed possible to create a useful machine-learning-based interatomic potential that comprehensively describes a material, and serves as a template for the development of such models in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum chemistry