Machine learning for quantum matter IV
FOCUS · S39 · ID: 354886
Presentations
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Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machines
Invited
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Presenters
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Estelle Inack
- Perimeter Inst for Theo Phys
Authors
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Sebastiano Pilati
- University of Camerino
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Estelle Inack
- Perimeter Inst for Theo Phys
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Pierbiagio Pieri
- University of Camerino
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Self-learning Hybrid Monte Carlo method for first-principles molecular simulations
ORAL
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Presenters
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Yuki Nagai
- JAEA
- Japan Atomic Energy Agency
Authors
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Yuki Nagai
- JAEA
- Japan Atomic Energy Agency
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Masahiko Okumura
- Japan Atomic Energy Agency
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Keita Kobayashi
- RIST
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Motoyuki Shiga
- Japan Atomic Energy Agency
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On-the-fly machine learning algorithm for accelerating Monte Carlo sampling: Application to the stochastic analytical continuation
ORAL
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Presenters
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Hongkee Yoon
- Korea Adv Inst of Sci & Tech
- KAIST
Authors
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Hongkee Yoon
- Korea Adv Inst of Sci & Tech
- KAIST
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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
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Presenters
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Shixin Zhang
- Tsinghua University
Authors
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Shixin Zhang
- Tsinghua University
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Zhou-Quan Wan
- Tsinghua University
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Hong Yao
- Tsinghua University
- Institute for Advanced Study, Tsinghua University
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Automatic Differentiable Monte Carlo: Applications
ORAL
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Presenters
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Zhou-Quan Wan
- Tsinghua University
Authors
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Zhou-Quan Wan
- Tsinghua University
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Shixin Zhang
- Tsinghua University
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Hong Yao
- Tsinghua University
- Institute for Advanced Study, Tsinghua University
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Optimal Real-Space Renormalization-Group Transformations with Artificial Neural Networks
ORAL
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Presenters
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Ying-Jer Kao
- Natl Taiwan Univ
Authors
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Jui-Hui Chung
- Natl Taiwan Univ
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Ying-Jer Kao
- Natl Taiwan Univ
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Machine-learning-accelerated predictions of optical properties of condensed systems based on many-body perturbation theory
ORAL
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Presenters
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Sijia Dong
- Materials Science Division, Argonne National Laboratory
Authors
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Sijia Dong
- Materials Science Division, Argonne National Laboratory
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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
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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
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Presenters
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Erica Sturm
- Brookhaven National Laboratory
Authors
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Erica Sturm
- Brookhaven National Laboratory
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Matthew R Carbone
- Department of Chemistry, Coumbia University
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Deyu Lu
- Brookhaven National Laboratory
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Andreas Weichselbaum
- Brookhaven National Laboratory
- Department of Condensed Matter Physics and Materials Science, Brookhaven National Laboratory
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Robert Konik
- CMPMSD, Brookhaven National Laboratory
- Brookhaven National Laboratory
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Finding New Mixing Strategies for Self Consistent Field Procedures Using Reinforcement Learning
ORAL
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Presenters
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Daniel Abarbanel
- McGill Univ
Authors
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Daniel Abarbanel
- McGill Univ
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Hong Guo
- McGill Univ
- Department of Physics, 3600 University, McGill University, Montreal, Quebec H3A 2T8, Canada
- Physics, McGill University
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Machine learning spin dynamics in the double-exchange systems
ORAL
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Presenters
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Puhan Zhang
- Univ of Virginia
Authors
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Puhan Zhang
- Univ of Virginia
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Preetha Saha
- Univ of Virginia
- Physics, University of Virginia
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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
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Presenters
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Linda Hung
- Toyota Research Institute
Authors
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Linda Hung
- Toyota Research Institute
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Daniel Schweigert
- Toyota Research Institute
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Arjun Bhargava
- Toyota Research Institute
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Chirranjeevi Gopal
- Toyota Research Institute
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Machine learning as a solution to the electronic structure problem
ORAL
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Presenters
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Beatriz Gonzalez del Rio
- School of Materials Science and Engineering, Georgia Institute of Technology
- Univ de Valladolid
Authors
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Beatriz Gonzalez del Rio
- School of Materials Science and Engineering, Georgia Institute of Technology
- Univ de Valladolid
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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
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Presenters
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Nina Andrejevic
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology MIT
Authors
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Nina Andrejevic
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology MIT
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Jovana Andrejevic
- Harvard University
- School of Engineering and Applied Sciences, Harvard University
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Christopher Rycroft
- Harvard University
- School of Engineering and Applied Sciences, Harvard University
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Mingda Li
- Massachusetts Institute of Technology
- Massachusetts Institute of Technology MIT
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