Machine Learning for Quantum Matter IV

Presentations

• m* of electron gases: a neural canonical transformation study

  ORAL · Invited

  March 17, 2022, 3:00 PM – March 17, 2022, 3:36 PM

  Presenters

  • Lei Wang

    • Institute of Physics

  Authors

  • Lei Wang

    • Institute of Physics

  • Hao Xie

    • Institute of Physics, Chinese Academy of Sciences

  • Linfeng Zhang

    • DP Technology Beijing 10080; AI for Science Institute, Beijing 10080
    • Beijing Institute of Big Data Research (BIBDR)

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• Calculation of nuclear ground states up to A=6 using Artificial Neural Networks.

  ORAL · Invited

  March 17, 2022, 3:36 PM – March 17, 2022, 4:12 PM

  Presenters

  • Corey Adams

    • Argonne National Lab

  Authors

  • Corey Adams

    • Argonne National Lab

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• Detecting topological order using recurrent neural network wave functions

  ORAL

  March 17, 2022, 4:12 PM – March 17, 2022, 4:24 PM

  Presenters

  • Mohamed Hibat-Allah

    • University of Waterloo

  Authors

  • Mohamed Hibat-Allah

    • University of Waterloo

  • Roger G Melko

    • University of Waterloo

  • Juan Carrasquilla

    • Vector Institute for Artificial Intelligence

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• Exploring variational methods with interpretable neural-networks and genetic algorithms

  ORAL

  March 17, 2022, 4:24 PM – March 17, 2022, 4:36 PM

  Publication: A. Valenti, E. Greplova, N. H. Lindner, and S. D. Huber. Correlation-enhanced neural networks as interpretable variational
  quantum states. arXiv preprint arXiv:2103.05017, 2021

  Presenters

  • Agnes Valenti

    • ETH Zurich

  Authors

  • Agnes Valenti

    • ETH Zurich

  • Eliska Greplova

    • Delft University of Technology

  • Netanel Lindner

    • Technion - Israel Institute of Technology

  • Evert Van Nieuwenburg

    • Niels Bohr International Academy, University of Copenhagen

  • Sebastian Huber

    • ETH Zurich

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• Deep Learning the Functional Renormalization Group Flow for Correlated Fermions

  ORAL

  March 17, 2022, 4:36 PM – March 17, 2022, 4:48 PM

  Presenters

  • Matija Medvidović

    • Columbia University

  Authors

  • Domenico Di Sante

    • University of Bologna
    • Center for Computational Quantum Physics, Flatiron Institute

  • Matija Medvidović

    • Columbia University

  • Alessandro Toschi

    • Institute of Solid State Physics Vienna

  • Giorgio Sangiovanni

    • Julius-Maximilians University of Wuerzbu
    • Julius-Maximilians University of Wuerzburg

  • Cesare Franchini

    • University of Vienna
    • Univ of Vienna
    • Univ of Vienna, Univ of Bologna
    • Universita' di Bologna & University of Vienna
    • University of Vienna, A-1090 Vienna, Austria, Alma Mater Studiorum–Università di Bologna, Bologna, 40127, Italy

  • Anirvan M Sengupta

    • Rutgers University, New Brunswick

  • Andrew J Millis

    • Columbia University
    • Columbia University; Flatiron Institute
    • Columbia University, Flatiron Institute

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• Predicting Quasiparticle and Excitonic properties of materials using Machine Learning

  ORAL

  March 17, 2022, 4:48 PM – March 17, 2022, 5:00 PM

  Presenters

  • Tathagata Biswas

    • Arizona State University

  Authors

  • Tathagata Biswas

    • Arizona State University

  • Sydney N Olson

    • Arizona State University

  • Arunima K Singh

    • Arizona State University
    • ASU

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• Quantum process tomography with neural networks.

  ORAL

  March 17, 2022, 5:00 PM – March 17, 2022, 5:12 PM

  Presenters

  • Shahnawaz Ahmed

    • Chalmers Univ of Tech

  Authors

  • Shahnawaz Ahmed

    • Chalmers Univ of Tech

  • Isaac Quijandria Diaz

    • Chalmers Univ of Tech
    • Chalmers University of Technology

  • Anton F Kockum

    • Chalmers Univ of Tech
    • Chalmers University of Technology

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• Thermal Transport Simulation of Strongly Anharmonic GeSe through Machine Learning

  ORAL

  March 17, 2022, 5:12 PM – March 17, 2022, 5:24 PM

  Presenters

  • Jie-Cheng Chen

    • Institute of Atomic and Molecular Sciences, Academia Sinica, Taiwan

  Authors

  • Mei-Yin Chou

    • Institute of Atomic and Molecular Sciences, Academia Sinica, Taiwan
    • Academia Sinica, Taiwan
    • Academia Sinica

  • Jie-Cheng Chen

    • Institute of Atomic and Molecular Sciences, Academia Sinica, Taiwan

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• Machine Learning the Relationship Between Debye and Superconducting Transition Temperatures

  ORAL

  March 17, 2022, 5:24 PM – March 17, 2022, 5:36 PM

  Presenters

  • Cheng-Chien Chen

    • University of Alabama at Birmingham

  Authors

  • Cheng-Chien Chen

    • University of Alabama at Birmingham

  • Adam D. Smith

    • University of Alabama at Birmingham

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• Transferable Machine Learning for Four-Dimensional Scanning Transmission Electron Microscopy Data

  ORAL

  March 17, 2022, 5:36 PM – March 17, 2022, 5:48 PM

  Presenters

  • Michael Matty

    • Cornell University

  Authors

  • Michael Matty

    • Cornell University

  • Michael Cao

    • Cornell University

  • Zhen Chen

    • Cornell University

  • Li Li

    • Google Research
    • Google LLC

  • David A Muller

    • Cornell University
    • School of Applied and Engineering Physics, Cornell University

  • Eun-Ah Kim

    • Cornell University

  View abstract →