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Machine Learning for Quantum Matter V

FOCUS · L21 · ID: 381558

Presentations

  • Interpretable and unsupervised phase classification based on averaged input features

    ORAL

    –

    Presenters

    • Julian Arnold

      Department of Physics, University of Basel

    Authors

    • Julian Arnold

      Department of Physics, University of Basel

    • Frank Schäfer

      Department of Physics, University of Basel, University of Basel

    • Martin Zonda

      Institute of Physics, Albert-Ludwigs-Universität Freiburg

    • Axel U. J. Lode

      University of Freiburg, Institute of Physics, Albert-Ludwig University of Freiburg, Institute of Physics, Albert-Ludwigs-Universität Freiburg

    View abstract →

  • Exploration of Topological Metamaterial Band Structures and Chern numbers using Deep Learning

    ORAL

    –

    Presenters

    • Vittorio Peano

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

    Authors

    • Vittorio Peano

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

    • Florian Sapper

      Max Planck Inst for Sci Light

    • Florian Marquardt

      Univ Erlangen Nuremberg, Max Planck Inst for Sci Light, Max Planck Institute for the Science of Light

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  • Unsupervised learning of topological order

    ORAL

    –

    Presenters

    • Gebremedhin Dagnew

      Middlebury College

    Authors

    • Gebremedhin Dagnew

      Middlebury College

    • Owen Myers

      Hometap

    • Chris M Herdman

      Middlebury College, Physics, Middlebury College

    • Lauren Haywards

      Perimeter Institute for Theoretical Physics

    View abstract →

  • Machine learning augmented neutron and x-ray scattering for quantum materials

    Invited

    –

    Presenters

    • Mingda Li

      Nuclear Science and Engineering, Massachusetts Institute of Technology, Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

    Authors

    • Mingda Li

      Nuclear Science and Engineering, Massachusetts Institute of Technology, Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

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  • Topological quantum phase transitions retrieved through unsupervised machine learning

    ORAL

    –

    Presenters

    • Yanming Che

      RIKEN

    Authors

    • Yanming Che

      RIKEN

    • Clemens Gneiting

      RIKEN, Japan, RIKEN

    • Tao Liu

      RIKEN

    • Franco Nori

      RIKEN, Japan and Univ. Michigan, USA, RIKEN, Japan, RIKEN; and Univ. Michigan., RIKEN, Japan; and Univ. Michigan, USA, Riken Japan and Univ. Michigan USA, RIKEN, Japan and Univ Michigan, USA, Theoretical Quantum Physics Laboratory, Department of Physics, RIKEN Cluster for Pioneering Research, The University of Michigan, RIKEN and Univ. of Michigan, Riken Japan and Univ Michigan USA, RIKEN; and University of Michigan, RIKEN and Univ. Michigan, RIKEN and Univ of Michigan, Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Saitama 351-0198, Japan, RIKEN, and University of Michigan, Theoretical Quantum Physics, Riken, Japan, RIKEN, Japan; and Univ Michigan, USA, Theoretical Quantum Physics Laboratory, RIKEN, RIKEN, Japan; Univ. Michigan, USA, RIKEN, Japan; Uni. Michigan, USA

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  • Machine learning spectral indicators of topology

    ORAL

    –

    Presenters

    • Nina Andrejevic

      Nuclear Science and Engineering, Massachusetts Institute of Technology, Massachusetts Institute of Technology

    Authors

    • Nina Andrejevic

      Nuclear Science and Engineering, Massachusetts Institute of Technology, Massachusetts Institute of Technology

    • Jovana Andrejevic

      Harvard University

    • Christopher Rycroft

      Harvard University

    • Mingda Li

      Nuclear Science and Engineering, Massachusetts Institute of Technology, Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

    View abstract →

  • AI-guided engineering of nanoscale topological materials

    ORAL

    –

    Presenters

    • Srilok Srinivasan

      Argonne National Laboratory, Center for Nanoscale Materials, Argonne National Laboratory

    Authors

    • Srilok Srinivasan

      Argonne National Laboratory, Center for Nanoscale Materials, Argonne National Laboratory

    • Mathew Cherukara

      Argonne National Laboratory

    • David Eckstein

      Argonne National Laboratory

    • Anthony Avarca

      Argonne National Laboratory

    • Subramanian Sankaranarayanan

      Argonne National Laboratory, Center for Nanoscale Materials, Argonne National Laboratory

    • Pierre Darancet

      Argonne National Laboratory, Center for Nanoscale Materials, Argonne National Laboratory, Center for Nanoscale Materials, Argonne National Lab

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  • Automatic Learning of Topological Phase Boundaries

    ORAL

    –

    Presenters

    • Alexander Kerr

      Center for Quantum Research and Technology, Univ of Oklahoma

    Authors

    • Alexander Kerr

      Center for Quantum Research and Technology, Univ of Oklahoma

    • Geo Jose

      Univ of Oklahoma, Center for Quantum Research and Technology, Univ of Oklahoma

    • Colin J Riggert

      Center for Quantum Research and Technology, Univ of Oklahoma

    • Kieran Mullen

      Center for Quantum Research and Technology, Univ of Oklahoma

    View abstract →