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Machine Learning for Quantum Matter II

FOCUS · S47 · ID: 46903

Presentations

  • Towards interpretable and reliable machines learning physics

    ORAL · Invited

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    Publication: [1] A. Dawid et al. (2020). Phase detection with neural networks: interpreting the black box. New J. Phys. 22, 115001.<br>[2] N. Käming, A. Dawid, K. Kottmann, et al. (2021). Unsupervised machine learning of topological phase transitions from experimental data. Mach. Learn.: Sci. Technol. 2, 035037.<br>[3] A. Dawid et al. (2021). Hessian-based toolbox for interpretable and reliable machine learning in physics. Mach. Learn.: Sci. Technol. in press https://doi.org/10.1088/2632-2153/ac338d.

    Presenters

    • Anna Dawid

      University of Warsaw & ICFO - The Institute of Photonic Sciences

    Authors

    • Anna Dawid

      University of Warsaw & ICFO - The Institute of Photonic Sciences

    • Patrick Huembeli

      École Polytechnique Fédérale de Lausanne

    • Michał Tomza

      University of Warsaw

    • Maciej Lewenstein

      ICFO - The Institute of Photonic Sciences & ICREA, ICFO / ICREA

    • Alexandre Dauphin

      ICFO - The Institute of Photonic Sciences

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  • Direct sampling of projected entangled-pair states

    ORAL

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    Publication: https://arxiv.org/abs/2109.07356

    Presenters

    • Tom Vieijra

      Ghent University

    Authors

    • Tom Vieijra

      Ghent University

    • Jutho Haegeman

      Ghent University

    • Frank Verstraete

      Ghent University

    • Laurens Vanderstraeten

      Ghent University

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  • Neural Network Ansatz for Finite Temperature

    ORAL

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    Publication: F. Vicentini, R. Rossi, G. Carleo, under preparation (2022)

    Presenters

    • Filippo Vicentini

      Ecole Polytechnique Federale de Lausanne

    Authors

    • Filippo Vicentini

      Ecole Polytechnique Federale de Lausanne

    • Riccardo Rossi

      Ecole Polytechnique Federale de Lausanne

    • Giuseppe Carleo

      Ecole Polytechnique Federale de Lausanne

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  • Ground-state properties via machine learning quantum constraints

    ORAL

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    Publication: P.-L. Zheng#, S.-J. Du#, and Y. Zhang*, "Ground-state properties via machine learning quantum constraints," (2021), arXiv:2105.09947 [cond-mat.str-el].<br>

    Presenters

    • Pei-Lin Zheng

      Peking Univ

    Authors

    • Pei-Lin Zheng

      Peking Univ

    • Si-Jing Du

      Peking Univ

    • Yi Zhang

      Peking Univ

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  • Gauge invariant autoregressive neural network for quantum lattice models

    ORAL

    –

    Publication: arXiv:2101.07243

    Presenters

    • Zhuo Chen

      Massachusetts Institute of Technology

    Authors

    • Zhuo Chen

      Massachusetts Institute of Technology

    • Di Luo

      Massachusetts Institute of Technology, University of Illinois at Urbana-Champaign

    • Kaiwen Hu

      University of Michigan—Ann Arbor

    • Zhizhen Zhao

      University of Illinois at Urbana-Champaign

    • Vera M Hur

      University of Illinois at Urbana-Champaign

    • Bryan K Clark

      University of Illinois at Urbana-Champaign

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  • Machine learning frequency-resolved phonon transport from ultrafast electron diffraction

    ORAL

    –

    Presenters

    • Zhantao Chen

      Massachusetts Institute of Technology MI, Massachusetts Institute of Technology

    Authors

    • Zhantao Chen

      Massachusetts Institute of Technology MI, Massachusetts Institute of Technology

    • Nina Andrejevic

      Massachusetts Institute of Technology MI

    • Tongtong Liu

      Massachusetts Institute of Technology MI

    • Xiaozhe Shen

      SLAC National Accelerator Laboratory, SLAC, SLAC Natl Accelerator Lab

    • Thanh Nguyen

      Massachusetts Institute of Technology MI

    • Nathan C Drucker

      Harvard University

    • Mingda Li

      Massachusetts Institute of Technology, Massachusetts Institute of Technology MI

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  • Interpretable Machine Learning for Materials Design

    ORAL

    –

    Presenters

    • Timur Bazhirov

      Exabyte Inc.

    Authors

    • Timur Bazhirov

      Exabyte Inc.

    • James Dean

      Exabyte Inc.

    • Rahul Bhowmik

      Polaron Analytics

    • Sergey Barabash

      Intermolecular, Inc.

    • Matthias Scheffler

      NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society, Fritz-Haber Institute, The NOMAD Laboratory at the Fritz Haber Institute of the MPG

    • Thomas A Purcell

      Fritz-Haber-Institute, Fritz-Haber Institute, The NOMAD Laboratory at the Fritz Haber Institute of the MPG

    View abstract →