Machine learning for quantum matter IV

Presentations

• Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machines

  Invited

  March 5, 2020, 1:15 PM – March 5, 2020, 1:51 PM

  Presenters

  • Estelle Inack

    Perimeter Inst for Theo Phys

  Authors

  • Sebastiano Pilati

    University of Camerino

  • Estelle Inack

    Perimeter Inst for Theo Phys

  • Pierbiagio Pieri

    University of Camerino

  View abstract →

• Self-learning Hybrid Monte Carlo method for first-principles molecular simulations

  ORAL

  March 5, 2020, 1:51 PM – March 5, 2020, 2:03 PM

  Presenters

  • Yuki Nagai

    JAEA, Japan Atomic Energy Agency

  Authors

  • Yuki Nagai

    JAEA, Japan Atomic Energy Agency

  • Masahiko Okumura

    Japan Atomic Energy Agency

  • Keita Kobayashi

    RIST

  • Motoyuki Shiga

    Japan Atomic Energy Agency

  View abstract →

• On-the-fly machine learning algorithm for accelerating Monte Carlo sampling: Application to the stochastic analytical continuation

  ORAL

  March 5, 2020, 2:03 PM – March 5, 2020, 2:15 PM

  Presenters

  • Hongkee Yoon

    Korea Adv Inst of Sci & Tech, KAIST

  Authors

  • Hongkee Yoon

    Korea Adv Inst of Sci & Tech, KAIST

  • Myung Joon Han

    Department of Physics, KAIST, Korea Adv Inst of Sci & Tech, Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), KAIST

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• Automatic Differentiable Monte Carlo: Theory

  ORAL

  March 5, 2020, 2:15 PM – March 5, 2020, 2:27 PM

  Presenters

  • Shixin Zhang

    Tsinghua University

  Authors

  • Shixin Zhang

    Tsinghua University

  • Zhou-Quan Wan

    Tsinghua University

  • Hong Yao

    Tsinghua University, Institute for Advanced Study, Tsinghua University

  View abstract →

• Automatic Differentiable Monte Carlo: Applications

  ORAL

  March 5, 2020, 2:27 PM – March 5, 2020, 2:39 PM

  Presenters

  • Zhou-Quan Wan

    Tsinghua University

  Authors

  • Zhou-Quan Wan

    Tsinghua University

  • Shixin Zhang

    Tsinghua University

  • Hong Yao

    Tsinghua University, Institute for Advanced Study, Tsinghua University

  View abstract →

• Optimal Real-Space Renormalization-Group Transformations with Artificial Neural Networks

  ORAL

  March 5, 2020, 2:39 PM – March 5, 2020, 2:51 PM

  Presenters

  • Ying-Jer Kao

    Natl Taiwan Univ

  Authors

  • Jui-Hui Chung

    Natl Taiwan Univ

  • Ying-Jer Kao

    Natl Taiwan Univ

  View abstract →

• Machine-learning-accelerated predictions of optical properties of condensed systems based on many-body perturbation theory

  ORAL

  March 5, 2020, 2:51 PM – March 5, 2020, 3:03 PM

  Presenters

  • Sijia Dong

    Materials Science Division, Argonne National Laboratory

  Authors

  • Sijia Dong

    Materials Science Division, Argonne National Laboratory

  • Marco Govoni

    Materials Science Division, Argonne National Laboratory, Materials Science Division and Center for Molecular Engineering, Argonne National Laboratory, Argonne National Laboratory, Argonne National Lab, Argonne Natl Lab

  • Giulia Galli

    University of Chicago, Pritzker School of Molecular Engineering, University of Chicago, Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA, University of Chicago and Argonne National Laboratory, Pritzker School of Molecular Engineering, The University of Chicago

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• Machine Learned Spectral Functions for the Quantum Impurity Problem

  ORAL

  March 5, 2020, 3:03 PM – March 5, 2020, 3:15 PM

  Presenters

  • Erica Sturm

    Brookhaven National Laboratory

  Authors

  • Erica Sturm

    Brookhaven National Laboratory

  • Matthew R Carbone

    Department of Chemistry, Coumbia University

  • Deyu Lu

    Brookhaven National Laboratory

  • Andreas Weichselbaum

    Brookhaven National Laboratory, Department of Condensed Matter Physics and Materials Science, Brookhaven National Laboratory

  • Robert Konik

    CMPMSD, Brookhaven National Laboratory, Brookhaven National Laboratory

  View abstract →

• Finding New Mixing Strategies for Self Consistent Field Procedures Using Reinforcement Learning

  ORAL

  March 5, 2020, 3:15 PM – March 5, 2020, 3:27 PM

  Presenters

  • Daniel Abarbanel

    McGill Univ

  Authors

  • Daniel Abarbanel

    McGill Univ

  • Hong Guo

    McGill Univ, Department of Physics, 3600 University, McGill University, Montreal, Quebec H3A 2T8, Canada, Physics, McGill University

  View abstract →

• Machine learning spin dynamics in the double-exchange systems

  ORAL

  March 5, 2020, 3:27 PM – March 5, 2020, 3:39 PM

  Presenters

  • Puhan Zhang

    Univ of Virginia

  Authors

  • Puhan Zhang

    Univ of Virginia

  • Preetha Saha

    Univ of Virginia, Physics, University of Virginia

  • Gia-Wei Chern

    Department of Physics, University of Virginia, Univ of Virginia, Physics, University of Virginia

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• Machine learning of high-throughput DFT electron densities

  ORAL

  March 5, 2020, 3:39 PM – March 5, 2020, 3:51 PM

  Presenters

  • Linda Hung

    Toyota Research Institute

  Authors

  • Linda Hung

    Toyota Research Institute

  • Daniel Schweigert

    Toyota Research Institute

  • Arjun Bhargava

    Toyota Research Institute

  • Chirranjeevi Gopal

    Toyota Research Institute

  View abstract →

• Machine learning as a solution to the electronic structure problem

  ORAL

  March 5, 2020, 3:51 PM – March 5, 2020, 4:03 PM

  Presenters

  • Beatriz Gonzalez del Rio

    School of Materials Science and Engineering, Georgia Institute of Technology, Univ de Valladolid

  Authors

  • Beatriz Gonzalez del Rio

    School of Materials Science and Engineering, Georgia Institute of Technology, Univ de Valladolid

  • Ramamurthy Ramprasad

    Georgia Institute of Technology, School of Materials Science and Engineering, Georgia Institute of Technology, Department of Material Science and Technology, Georgia Tech, Materials Science and Engineering, Georgia Institute of Technology

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• Machine learning spectral indicators of topology

  ORAL

  March 5, 2020, 4:03 PM – March 5, 2020, 4:15 PM

  Presenters

  • Nina Andrejevic

    Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

  Authors

  • Nina Andrejevic

    Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

  • Jovana Andrejevic

    Harvard University, School of Engineering and Applied Sciences, Harvard University

  • Christopher Rycroft

    Harvard University, School of Engineering and Applied Sciences, Harvard University

  • Mingda Li

    Massachusetts Institute of Technology, Massachusetts Institute of Technology MIT

  View abstract →