Machine Learning in Condensed Matter Physics I

FOCUS · E34

Presentations

  • From Boltzmann machines to Born machines

    Invited

    –

    Presenters

    • Lei Wang

      Institute of Physics, Chinese Academy of Science, Chinese Academy of Sciences, Institute of Physics, Chinese Academy of Sciences

    Authors

    • Lei Wang

      Institute of Physics, Chinese Academy of Science, Chinese Academy of Sciences, Institute of Physics, Chinese Academy of Sciences

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  • Neural-network quantum state tomography

    ORAL

    –

    Presenters

    • Giacomo Torlai

      University of Waterloo

    Authors

    • Giacomo Torlai

      University of Waterloo

    • Guglielmo Mazzola

      ETH, ITP, ETH Zurich

    • Juan Carrasquilla

      Dwave, D-Wave INC

    • Matthias Troyer

      Microsoft Research, Quantum Architectures and Computation Group, Microsoft Research, Microsoft, ITP, ETH Zurich

    • Roger Melko

      Perimeter Institute for Theoretical Physics, University of Waterloo, Univ of Waterloo

    • Giuseppe Carleo

      Institute for Theoretical Physics, ETH, ETH, ITP, ETH Zurich

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  • Complexity and geometry of quantum state manifolds

    ORAL

    –

    Presenters

    • Zhoushen Huang

      Los Alamos National Laboratory, Institute for Materials Science, Los Alamos National Laboratory

    Authors

    • Zhoushen Huang

      Los Alamos National Laboratory, Institute for Materials Science, Los Alamos National Laboratory

    • Alexander Balatsky

      NORDITA, Institute for Materials Science, Los Alamos National Laboratory, Nordita, Los Alamos Natl Lab, Nordita, KTH Royal Institute of Technology and Stockholm University; Institute for Materials Science, Los Alamos National Laboratory; Department of Physics, University of Conn, Instittute for Materials Science, Los Alamos National Laboratory, Institute for Materials Science, Los Alamos National Laboratory/Nordita/University of Connecticut

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  • Recurrent Neural Networks for Quantum Feedback

    ORAL

    –

    Presenters

    • Talitha Weiss

      Max Planck Inst for the Science of Light, Max Planck Institute for the Science of Light, Max Planck Society, Max Planck Inst for Sci Light

    Authors

    • Thomas Foesel

      Max Planck Inst for the Science of Light, Max Planck Inst for Sci Light

    • Talitha Weiss

      Max Planck Inst for the Science of Light, Max Planck Institute for the Science of Light, Max Planck Society, Max Planck Inst for Sci Light

    • Petru Tighineanu

      The Max Planck Institute for the Science of Light, Max Planck Inst for the Science of Light, Max Planck Inst for Sci Light

    • Florian Marquardt

      Max Planck Inst for the Science of Light, Max Planck Inst for Sci Light, Max Planck Institute for the Science of Light

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  • Interaction Distance: Measuring Many-Body Freedom via Quantum Correlation Structure

    ORAL

    –

    Presenters

    • Konstantinos Meichanetzidis

      Theoretical Physics, Univ of Leeds

    Authors

    • Konstantinos Meichanetzidis

      Theoretical Physics, Univ of Leeds

    • Christopher Turner

      University of Leeds, Theoretical Physics, Univ of Leeds

    • Ashk Farjami

      Theoretical Physics, Univ of Leeds

    • Zlatko Papic

      University of Leeds, Physics, University of Leeds, Theoretical Physics, Univ of Leeds

    • Jiannis Pachos

      Theoretical Physics, Univ of Leeds

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  • Machine learning modeling of superconducting critical temperature

    ORAL

    –

    Presenters

    • Valentin Stanev

      University of Maryland

    Authors

    • Valentin Stanev

      University of Maryland

    • Corey Oses

      Duke University

    • A. Gilad Kusne

      NIST

    • Efrain Rodriguez

      University of Maryland, Department of Chemistry and Biochemistry, University of Maryland, Chemistry and Biochemistry , University of Maryland

    • Johnpierre Paglione

      Center for Nanophysics and Advanced Materials , University of Maryland, CNAM, Department of Physics, University of Maryland, Univ of Maryland-College Park, Department of Physics, University of Maryland, CNAM, Department of Physics, Univ of Maryland-College Park, Univ of Maryland - College Park, College Park, MD 20742-4111, Univ of Maryland-College Park, Center for Nanophysics and Advanced Materials, Department of Physics, University of Maryland, Center for Nanophysics and Advanced Materials, University of Maryland, University of Maryland, College Park, University of Maryland

    • Stefano Curtarolo

      Material Science, Duke University, Duke University, Material Science, Electrical Engineering, Physics and Chemistry, Duke University

    • Ichiro Takeuchi

      Materials Science and Engineering, University of Maryland, University of Maryland, Univ of Maryland-College Park, Materials Science and Engineering, Univ of Maryland

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  • Neural network prediction of Tc for conventional and unconventional superconductors

    ORAL

    –

    Presenters

    • Ethan Shapera

      Physics, Univ of Illinois - Urbana

    Authors

    • Ethan Shapera

      Physics, Univ of Illinois - Urbana

    • Suraj Dhanak

      Materials Science and Engineering, University of Illinois - Urbana

    • Andre Schleife

      University of Illinois at Urbana-Champaign, Materials Science and Engineering, Univ of Illinois - Urbana, Materials Science and Engineering, University of Illinois, Urbana-Champaign, Materials Science and Engineering, University of Illinois - Urbana, Department of Materials Science and Engineering, University of Illinois, Univ of Illinois at Urbana-Champaign, University of Illinois, University of Illinois at Urbana–Champaign

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  • Materials prediction using machine learning: comparing MBTR, MTP and deep learning

    ORAL

    –

    Presenters

    • Chandramouli Nyshadham

      Brigham Young University, Physics and Astronomy, Brigham Young University

    Authors

    • Chandramouli Nyshadham

      Brigham Young University, Physics and Astronomy, Brigham Young University

    • Wiley Morgan

      Brigham Young University, Physics and Astronomy, Brigham Young University

    • Brayden Bekker

      Physics and Astronomy, Brigham Young University

    • Gus Hart

      Brigham Young Univ - Provo, Brigham Young University, Physics and Astronomy, Brigham Young University

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  • Evaluation of Machine Learning Methods for the Prediction of Key Properties for Novel Transparent Semiconductors

    ORAL

    –

    Presenters

    • Christopher Sutton

      Fritz Haber Institute of the Max Planck Society, Theory , Fritz-Haber Institute, Chemistry, Duke University, Theory Department, Fritz Haber Institute

    Authors

    • Christopher Sutton

      Fritz Haber Institute of the Max Planck Society, Theory , Fritz-Haber Institute, Chemistry, Duke University, Theory Department, Fritz Haber Institute

    • Christopher Bartel

      University of Colorado, University of Colorado Boulder

    • Xiangyue Liu

      Theory , Fritz-Haber Institute

    • Mario Boley

      Max Planck Institute for Informatics

    • Matthias Rupp

      Theory , Fritz-Haber Institute

    • Luca Ghiringhelli

      Fritz Haber Institute of the Max Planck Society, Theory, Fritz Haber Institute of the Max Planck Society, Theory , Fritz-Haber Institute, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin-Dahlem, Germany, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Theory Department, Fritz Haber Institute

    • Matthias Scheffler

      Fritz Haber Institute of the Max Planck Society, Theory, Fritz Haber Institute of the Max Planck Society, Fritz-Haber-Institut der Max-Planck-Gesselschaft, Theory , Fritz-Haber Institute, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin-Dahlem, Germany, Theory Department, Fritz Haber Institute

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