Machine Learning in Condensed Matter Physics I
FOCUS · E34 ·
Presentations
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From Boltzmann machines to Born machines
Invited
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Presenters
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Lei Wang
- Institute of Physics, Chinese Academy of Science
- Chinese Academy of Sciences
- Institute of Physics, Chinese Academy of Sciences
Authors
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Lei Wang
- Institute of Physics, Chinese Academy of Science
- Chinese Academy of Sciences
- Institute of Physics, Chinese Academy of Sciences
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Neural-network quantum state tomography
ORAL
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Presenters
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Giacomo Torlai
- University of Waterloo
Authors
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Giacomo Torlai
- University of Waterloo
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Guglielmo Mazzola
- ETH
- ITP, ETH Zurich
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Juan Carrasquilla
- Dwave
- D-Wave INC
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Matthias Troyer
- Microsoft Research
- Quantum Architectures and Computation Group, Microsoft Research
- Microsoft
- ITP, ETH Zurich
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Roger Melko
- Perimeter Institute for Theoretical Physics
- University of Waterloo
- Univ of Waterloo
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Giuseppe Carleo
- Institute for Theoretical Physics, ETH
- ETH
- ITP, ETH Zurich
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Approximating quantum many-body wave-functions using artificial neural networks
ORAL
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Presenters
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Zi Cai
- Department of Physics and Astronomy, Shanghai Jiao Tong University
- Shanghai Jiao Tong Univ
Authors
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Zi Cai
- Department of Physics and Astronomy, Shanghai Jiao Tong University
- Shanghai Jiao Tong Univ
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Hunting for Hamiltonians: A Computational Approach to Learning Quantum Models
ORAL
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Presenters
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Eli Chertkov
- University of Illinois at Urbana-Champaign
Authors
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Eli Chertkov
- University of Illinois at Urbana-Champaign
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Bryan Clark
- Physics, University of Illinois at Urbana-Champaign
- University of Illinois
- University of Illinois at Urbana-Champaign
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Complexity and geometry of quantum state manifolds
ORAL
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Presenters
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Zhoushen Huang
- Los Alamos National Laboratory
- Institute for Materials Science, Los Alamos National Laboratory
Authors
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Zhoushen Huang
- Los Alamos National Laboratory
- Institute for Materials Science, Los Alamos National Laboratory
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Alexander Balatsky
- NORDITA
- Institute for Materials Science, Los Alamos National Laboratory
- Nordita
- Los Alamos Natl Lab
- Nordita, KTH Royal Institute of Technology and Stockholm University; Institute for Materials Science, Los Alamos National Laboratory; Department of Physics, University of Conn
- Instittute for Materials Science, Los Alamos National Laboratory
- Institute for Materials Science, Los Alamos National Laboratory/Nordita/University of Connecticut
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Recurrent Neural Networks for Quantum Feedback
ORAL
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Presenters
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Talitha Weiss
- Max Planck Inst for the Science of Light
- Max Planck Institute for the Science of Light, Max Planck Society
- Max Planck Inst for Sci Light
Authors
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Thomas Foesel
- Max Planck Inst for the Science of Light
- Max Planck Inst for Sci Light
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Talitha Weiss
- Max Planck Inst for the Science of Light
- Max Planck Institute for the Science of Light, Max Planck Society
- Max Planck Inst for Sci Light
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Petru Tighineanu
- The Max Planck Institute for the Science of Light
- Max Planck Inst for the Science of Light
- Max Planck Inst for Sci Light
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Florian Marquardt
- Max Planck Inst for the Science of Light
- Max Planck Inst for Sci Light
- Max Planck Institute for the Science of Light
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Interaction Distance: Measuring Many-Body Freedom via Quantum Correlation Structure
ORAL
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Presenters
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Konstantinos Meichanetzidis
- Theoretical Physics, Univ of Leeds
Authors
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Konstantinos Meichanetzidis
- Theoretical Physics, Univ of Leeds
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Christopher Turner
- University of Leeds
- Theoretical Physics, Univ of Leeds
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Ashk Farjami
- Theoretical Physics, Univ of Leeds
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Zlatko Papic
- University of Leeds
- Physics, University of Leeds
- Theoretical Physics, Univ of Leeds
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Jiannis Pachos
- Theoretical Physics, Univ of Leeds
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Machine learning modeling of superconducting critical temperature
ORAL
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Presenters
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Valentin Stanev
- University of Maryland
Authors
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Valentin Stanev
- University of Maryland
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Corey Oses
- Duke University
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A. Gilad Kusne
- NIST
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Efrain Rodriguez
- University of Maryland
- Department of Chemistry and Biochemistry, University of Maryland
- Chemistry and Biochemistry , University of Maryland
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Johnpierre Paglione
- Center for Nanophysics and Advanced Materials , University of Maryland
- CNAM, Department of Physics, University of Maryland
- Univ of Maryland-College Park
- Department of Physics, University of Maryland
- CNAM, Department of Physics, Univ of Maryland-College Park
- Univ of Maryland - College Park
- College Park, MD 20742-4111, Univ of Maryland-College Park
- Center for Nanophysics and Advanced Materials, Department of Physics, University of Maryland
- Center for Nanophysics and Advanced Materials, University of Maryland
- University of Maryland, College Park
- University of Maryland
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Stefano Curtarolo
- Material Science, Duke University
- Duke University
- Material Science, Electrical Engineering, Physics and Chemistry, Duke University
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Ichiro Takeuchi
- Materials Science and Engineering, University of Maryland
- University of Maryland
- Univ of Maryland-College Park
- Materials Science and Engineering, Univ of Maryland
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Neural network prediction of Tc for conventional and unconventional superconductors
ORAL
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Presenters
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Ethan Shapera
- Physics, Univ of Illinois - Urbana
Authors
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Ethan Shapera
- Physics, Univ of Illinois - Urbana
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Suraj Dhanak
- Materials Science and Engineering, University of Illinois - Urbana
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Andre Schleife
- University of Illinois at Urbana-Champaign
- Materials Science and Engineering, Univ of Illinois - Urbana
- Materials Science and Engineering, University of Illinois, Urbana-Champaign
- Materials Science and Engineering, University of Illinois - Urbana
- Department of Materials Science and Engineering, University of Illinois
- Univ of Illinois at Urbana-Champaign
- University of Illinois
- University of Illinois at Urbana–Champaign
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Data-Driven Design of Nanoscale Features to Obtain High-zT Thermoelectrics
ORAL
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Presenters
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Emily Conant
- Texas A&M Univ
Authors
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Emily Conant
- Texas A&M Univ
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Timothy Brown
- Texas A&M Univ
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Raymundo Arroyave
- Texas A&M Univ
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Joseph Ross
- Texas A&M University
- Texas A&M Univ
- Physics And Astronomy, Texas A&M University
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Patrick Shamberger
- Texas A&M Univ
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Materials prediction using machine learning: comparing MBTR, MTP and deep learning
ORAL
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Presenters
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Chandramouli Nyshadham
- Brigham Young University
- Physics and Astronomy, Brigham Young University
Authors
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Chandramouli Nyshadham
- Brigham Young University
- Physics and Astronomy, Brigham Young University
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Wiley Morgan
- Brigham Young University
- Physics and Astronomy, Brigham Young University
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Brayden Bekker
- Physics and Astronomy, Brigham Young University
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Gus Hart
- Brigham Young Univ - Provo
- Brigham Young University
- Physics and Astronomy, Brigham Young University
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Evaluation of Machine Learning Methods for the Prediction of Key Properties for Novel Transparent Semiconductors
ORAL
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Presenters
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Christopher Sutton
- Fritz Haber Institute of the Max Planck Society
- Theory , Fritz-Haber Institute
- Chemistry, Duke University
- Theory Department, Fritz Haber Institute
Authors
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Christopher Sutton
- Fritz Haber Institute of the Max Planck Society
- Theory , Fritz-Haber Institute
- Chemistry, Duke University
- Theory Department, Fritz Haber Institute
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Christopher Bartel
- University of Colorado
- University of Colorado Boulder
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Xiangyue Liu
- Theory , Fritz-Haber Institute
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Mario Boley
- Max Planck Institute for Informatics
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Matthias Rupp
- Theory , Fritz-Haber Institute
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Luca Ghiringhelli
- Fritz Haber Institute of the Max Planck Society
- Theory, Fritz Haber Institute of the Max Planck Society
- Theory , Fritz-Haber Institute
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin-Dahlem, Germany
- Fritz-Haber-Institut der Max-Planck-Gesellschaft
- Theory Department, Fritz Haber Institute
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Matthias Scheffler
- Fritz Haber Institute of the Max Planck Society
- Theory, Fritz Haber Institute of the Max Planck Society
- Fritz-Haber-Institut der Max-Planck-Gesselschaft
- Theory , Fritz-Haber Institute
- Fritz-Haber-Institut der Max-Planck-Gesellschaft
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin-Dahlem, Germany
- Theory Department, Fritz Haber Institute
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A robust artificial neural network potential for Si(001)
ORAL
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Presenters
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Duy Le
- Physics, University of Central Florida
Authors
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Duy Le
- Physics, University of Central Florida
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Talat Rahman
- Physics, University of Central Florida
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