APS Logo

Machine Learning for Quantum Matter VI

FOCUS · W39 · ID: 355144

Presentations

  • Differentiable programming tensor networks and quantum circuits

    Invited

    –

    Presenters

    • JinGuo Liu

      Institute of Physics

    Authors

    • JinGuo Liu

      Institute of Physics

    • Lei Wang

      Institute of Physics, Institute of Physics, The Chinese Academy of Sciences, Chinese Academy of Sciences,Institute of Physics, Institute of Physics, Chinese Academy of Sciences

    View abstract →

  • Direct and Reverse Structure-Electronic Property Relationship Prediction with Deep Learning and Bayesian Optimization

    ORAL

    –

    Presenters

    • Artem Pimachev

      Aerospace Engineering, University of Colorado at Boulder, Univ of Wyoming

    Authors

    • Artem Pimachev

      Aerospace Engineering, University of Colorado at Boulder, Univ of Wyoming

    • Sanghamitra Neogi

      Aerospace Engineering, University of Colorado at Boulder, University of Colorado, Boulder, Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder

    View abstract →

  • Machine Learning of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-Picometer Precision

    ORAL

    –

    Presenters

    • Abid Khan

      University of Illinois at Urbana-Champaign

    Authors

    • Abid Khan

      University of Illinois at Urbana-Champaign

    • Bryan Clark

      University of Illinois at Urbana-Champaign

    • Chia-Hao Lee

      University of Illinois at Urbana-Champaign

    • Di Luo

      University of Illinois at Urbana-Champaign

    • Chuqiao Shi

      University of Illinois at Urbana-Champaign

    • Sangmin Kang

      University of Illinois at Urbana-Champaign

    • Wenjuan Zhu

      University of Illinois at Urbana-Champaign

    • Pinshane Huang

      University of Illinois at Urbana-Champaign

    View abstract →

  • Dictionary Learning in Fourier Transform Scanning Tunneling Spectroscopy

    ORAL

    –

    Presenters

    • Yenson Lau

      Columbia Univ

    Authors

    • Jedrzej Wieteska

      Columbia Univ, Physics, Columbia University

    • Yenson Lau

      Columbia Univ

    • Tetsuo Hanaguri

      Center for Emergent Matter Science, RIKEN, RIKEN, CEMS, RIKEN, RIKEN CEMS

    • John Wright

      Columbia Univ

    • Ilya Eremin

      Institute for Theoretical Physics, Ruhr-Universität Bochum, Ruhr Univ Bochum

    • Abhay Pasupathy

      Columbia University, Physics Department, Columbia University, Columbia Univ, Department of Physics, Columbia University, New York, New York 10027, USA, Physics, Columbia University, Department of Physics, Columbia University

    View abstract →

  • Machine Learning Tool for Crystal Structure Predictions

    ORAL

    –

    Presenters

    • Valentin Stanev

      University of Maryland, College Park

    Authors

    • Valentin Stanev

      University of Maryland, College Park

    • Haotong Liang

      University of Maryland, College Park

    • Aaron Kusne

      National Institute of Standards and Technology, Gaithersburg, MD

    • Ichiro Takeuchi

      University of Maryland, College Park

    View abstract →

  • Transferable and interpretable machine learning model for four-dimensional scanning transmission electron microscopy data

    ORAL

    –

    Presenters

    • Michael Matty

      Physics, Cornell University, Cornell University

    Authors

    • Michael Matty

      Physics, Cornell University, Cornell University

    • Michael Cao

      Cornell University, Applied and Engineering Physics, Cornell University

    • Zhen Chen

      Applied and Engineering Physics, Cornell University, Cornell University, School of Applied and Engineering Physics, Cornell University

    • Li Li

      Google Research

    • David Muller

      Cornell University, School of Applied and Engineering Physics, Cornell University, Applied and Engineering Physics, Cornell University

    View abstract →

  • Tight-binding deep learning approach to band structures calculations

    ORAL

    –

    Presenters

    • Florian Sapper

      Max Planck Inst for Sci Light

    Authors

    • Florian Sapper

      Max Planck Inst for Sci Light

    • Vittorio Peano

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

    • Florian Marquardt

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

    View abstract →