Machine Learning in Condensed Matter Physics IV

FOCUS · R34

Presentations

  • Tensor Network Machine Learning Models

    Invited

    –

    Presenters

    • Edwin Stoudenmire

      Center for Computational Quantum Physics, Flatiron Institute, University of California - Irvine, Department of Physics and Astronomy, University of California at Irvine

    Authors

    • Edwin Stoudenmire

      Center for Computational Quantum Physics, Flatiron Institute, University of California - Irvine, Department of Physics and Astronomy, University of California at Irvine

    View abstract →

  • Efficient Representation of Matrx Product State with Restricted Boltzmann Machine

    ORAL

    –

    Presenters

    • Zhengyu Zhang

      Department of Physics, Univ of Michigan - Ann Arbor

    Authors

    • Zhengyu Zhang

      Department of Physics, Univ of Michigan - Ann Arbor

    • Xun Gao

      Center for Quantum Information, IIIS, Tsinghua University

    • Luming Duan

      Department of Physics, University of Michigan, Tsinghua Univ, Department of Physics, Univ of Michigan - Ann Arbor, Tsinghua University, IIIS, Center for Quantum Information, University of Michigan

    View abstract →

  • Morse-Smale Systems and Machine Learning

    ORAL

    –

    Presenters

    • Kyle Kawagoe

      Physics, University of Chicago

    Authors

    • Kyle Kawagoe

      Physics, University of Chicago

    • Arvind Murugan

      Physics, University of Chicago, University of Chicago, James Franck Institute, University of Chicago

    View abstract →

  • Self-learning Monte Carlo Method with Deep Neural Networks

    ORAL

    –

    Presenters

    • Junwei Liu

      Massachusetts Inst of Tech-MIT, Physics, Hong Kong University of Science and Technology, Physics, MIT

    Authors

    • Junwei Liu

      Massachusetts Inst of Tech-MIT, Physics, Hong Kong University of Science and Technology, Physics, MIT

    • Huitao Shen

      Physics, Massachusetts Inst of Technology, Massachusetts Institute of Technology

    • Liang Fu

      Department of Physics, Massachusetts Institute of Technology, Massachusetts Inst of Tech-MIT, Physics, Massachusetts Inst of Tech-MIT, Physics, Massachusetts Institute of Technology, Physics, Massachusetts Inst of Technology, Physics, MIT, Massachusetts Institute of Technology, MIT

    View abstract →

  • SchNet - A Deep Learning Architecture for Molecules and Materials

    ORAL

    –

    Presenters

    • Alexandre Tkatchenko

      Université du Luxembourg, University of Luxembourg, Physics and Materials Science Research Unit, University of Luxembourg, Physics and Materials Science Research Unit,, University of Luxembourg

    Authors

    • Kristof Schütt

      TU Berlin

    • Huziel Sauceda

      Fritz-Haber-Institut der Max-Planck-Gesellschaft

    • Pieter-Jan Kindermans

      TU Berlin

    • Stefan Chmiela

      TU Berlin, Technische Universität Berlin

    • Klaus-Robert Müller

      TU Berlin, Technische Universität Berlin

    • Alexandre Tkatchenko

      Université du Luxembourg, University of Luxembourg, Physics and Materials Science Research Unit, University of Luxembourg, Physics and Materials Science Research Unit,, University of Luxembourg

    View abstract →

  • Deep Potential Molecular Dynamics: a Scalable Model with the Accuracy of Quantum Mechanics

    ORAL

    –

    Presenters

    • Linfeng Zhang

      Program in Applied and Computational Mathmatics, Princeton University

    Authors

    • Linfeng Zhang

      Program in Applied and Computational Mathmatics, Princeton University

    • Jiequn Han

      Program in Applied and Computational Mathmatics, Princeton University

    • Han Wang

      Institute of Applied Physics and Computational Mathematics

    • Roberto Car

      Department of Chemistry, Princeton, Department of Chemistry, Princeton Univ, Department of Chemistry , Princeton University, Princeton University, Physics, Princeton University, Department of Chemistry, Princeton University

    • Weinan E

      Program in Applied and Computational Mathmatics, Princeton University

    View abstract →