Machine Learning for Quantum Matter II

Presentations

• A Bayesian machine-learning approach to the quantum many-body problemInvited Talk: George Booth, King's College London

  ORAL · Invited

  March 7, 2023, 11:00 AM – March 7, 2023, 11:36 AM

  Publication: • Gaussian process states: A data-driven representation of quantum many-body physics; A Glielmo, Y Rath, G Csányi, A De Vita, GH Booth, Physical Review X, 10, 041026 (2020).<br>• A Bayesian inference framework for compression and prediction of quantum states; Y Rath, A Glielmo, GH Booth, Journal of Chemical Physics, 153, 124108 (2020).<br>• Quantum Gaussian process state: A kernel-inspired state with quantum support data; Y Rath, GH Booth, Physical Review Research, 4, 023126 (2022).<br>

  Presenters

  • George Booth

    King's College London, Kings College London

  Authors

  • George Booth

    King's College London, Kings College London

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• Electronic excited states in deep variational Monte Carlo

  ORAL

  March 7, 2023, 11:36 AM – March 7, 2023, 11:48 AM

  Publication: Electronic excited states in deep variational Monte Carlo (submitted manuscript under review)<br>(arXiv:2203.09472)

  Presenters

  • Mike Entwistle

    Freie Univ Berlin

  Authors

  • Mike Entwistle

    Freie Univ Berlin

  • Zeno Schätzle

    Freie Univ Berlin

  • Paolo A Erdman

    Freie Universität Berlin, Freie Univ Berlin

  • Jan Hermann

    Freie Univ Berlin

  • Frank Noe

    Freie Univ Berlin

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• Improving Machine Learning Modelling of Physical Properties with Isometry Invariants

  ORAL

  March 7, 2023, 11:48 AM – March 7, 2023, 12:00 PM

  Presenters

  • Alya Alqaydi

    Univ of Cambridge

  Authors

  • Alya Alqaydi

    Univ of Cambridge

  • Bartomeu Monserrat

    University of Cambridge, Univ of Cambridge

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• Machine Learning Model of Generalized Force Field in Condensed Matter Systems

  ORAL

  March 7, 2023, 12:00 PM – March 7, 2023, 12:12 PM

  Presenters

  • Gia-Wei Chern

    University of Virginia

  Authors

  • Gia-Wei Chern

    University of Virginia

  • Puhan Zhang

    University of Virginia

  • Sheng Zhang

    University of Virginia

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• Improvements to Neural Network Backflow Wavefunctions

  ORAL

  March 7, 2023, 12:12 PM – March 7, 2023, 12:24 PM

  Presenters

  • Zejun Liu

    University of Illinois at Urbana-Champai

  Authors

  • Zejun Liu

    University of Illinois at Urbana-Champai

  • Bryan K Clark

    University of Illinois at Urbana-Champaign

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• Similarities and differences in flat-band models with randomness detected by machine learning

  ORAL

  March 7, 2023, 12:24 PM – March 7, 2023, 12:36 PM

  Presenters

  • Takumi Kuroda

    University of Tsukuba

  Authors

  • Takumi Kuroda

    University of Tsukuba

  • Tomonari Mizoguchi

    University of Tsukuba

  • Yasuhiro Hatsugai

    University of Tsukuba

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• Studying the Superfluid Ground-State of the Unitary Fermi Gas with Fermionic Neural Networks.

  ORAL

  March 7, 2023, 12:36 PM – March 7, 2023, 12:48 PM

  Presenters

  • Wan Tong Lou

    Imperial College London

  Authors

  • Wan Tong Lou

    Imperial College London

  • Gino W Cassella

    Imperial College London

  • Halvard Sutterud

    Imperial College London

  • W Matthew C Foulkes

    Imperial College London

  • Johannes Knolle

    TU Munich, Germany

  • David Pfau

    Deepmind, DeepMind

  • James Spencer

    Deepmind, DeepMind

  View abstract →

• Machine learning quantum Monte Carlo: application to water clusters

  ORAL

  March 7, 2023, 12:48 PM – March 7, 2023, 1:00 PM

  Presenters

  • Matteo Peria

    Sorbonne University

  Authors

  • Matteo Peria

    Sorbonne University

  • Michele Casula

    Institut de Minéralogie de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Museum National d'Histoire Naturelle, Paris, France, Sorbonne University, IMPMC, UMR 7590 CNRS - Sorbonne Université Paris

  • A. Marco Saitta

    Sorbonne université-IMPMC, Sorbonne University, Sorbonne Université - IMPMC

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• Inverse Hamiltonian design by automatic differentiation

  ORAL

  March 7, 2023, 1:00 PM – March 7, 2023, 1:12 PM

  Publication: K. Inui and Y. Motome, arXiv:2203.07157 (2022).<br>https://github.com/koji-inui/automatic-hamiltonian-design

  Presenters

  • Koji Inui

    RIKEN

  Authors

  • Koji Inui

    RIKEN

  • Yukitoshi Motome

    University of Tokyo, Univ of Tokyo, Univ. of Tokyo

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• Variational simulations of fermionic matter with neural-network quantum states

  ORAL

  March 7, 2023, 1:12 PM – March 7, 2023, 1:24 PM

  Publication: Variational solutions to fermion-to-qubit mappings in two spatial dimensions, Jannes Nys and Giuseppe Carleo, Quantum 6, 833 (2022). https://doi.org/10.22331/q-2022-10-13-833

  Presenters

  • Jannes Nys

    École Polytechnique Fédérale de Lausanne (EPFL)

  Authors

  • Jannes Nys

    École Polytechnique Fédérale de Lausanne (EPFL)

  • Giuseppe Carleo

    École polytechnique fédérale de Lausanne, EPFL

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• Langevin Dynamics/Monte Carlo Simulations of Nanoscale Dielectric Function Modulations of Moire Materials

  ORAL

  March 7, 2023, 1:24 PM – March 7, 2023, 1:36 PM

  Publication: "Langevin dynamics/Monte Carlo simulations method for calculating nanoscale dielectric functions of materials"<br>https://doi.org/10.1103/PhysRevMaterials.6.076001

  Presenters

  • Steven B Hancock

    University of Georgia

  Authors

  • Steven B Hancock

    University of Georgia

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• Machine learning universal empirical pseudopotentials for density functional theory calculations

  ORAL

  March 7, 2023, 1:36 PM – March 7, 2023, 1:48 PM

  Presenters

  • Rokyeon Kim

    Korea Institute for Advanced Study

  Authors

  • Rokyeon Kim

    Korea Institute for Advanced Study

  • Young-Woo Son

    Korea Inst for Advanced Study, Korea Institute for Advanced Study

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