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Machine Learning for Quantum Matter III

FOCUS · C21 · ID: 381557

Presentations

  • Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks

    ORAL

    –

    Presenters

    • Tess Smidt

      Lawrence Berkeley National Laboratory, Computational Research Division, Lawrence Berkeley National Laboratory

    Authors

    • Tess Smidt

      Lawrence Berkeley National Laboratory, Computational Research Division, Lawrence Berkeley National Laboratory

    • Mario Geiger

      École polytechnique fédérale de Lausanne, Ecole Polytechnique Federale de Lausanne

    • Benjamin Kurt Miller

      University of Amsterdam

    View abstract →

  • Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

    ORAL

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    Presenters

    • Sijia Dong

      Argonne National Laboratory, Materials Science Division, Argonne National Laboratory

    Authors

    • Sijia Dong

      Argonne National Laboratory, Materials Science Division, Argonne National Laboratory

    • Marco Govoni

      Materials Science Division and Center for Molecular Engineering, Argonne National Laboratory, Argonne National Laboratory, Materials Science Division, Argonne National Laboratory

    • Giulia Galli

      The University of Chicago, Pritzker School of Molecular Engineering, The University of Chicago, Pritzker School of Molecular Engineering, University of Chicago, University of Chicago, Department of Chemistry, University of Chicago, Materials Science Division and Center for Molecular Engineering, Argonne National Laboratory

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  • Generative Model Learning For Molecular Electronics

    ORAL

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    Presenters

    • Andrew Mitchell

      Univ Coll Dublin, Physics, University College Dublin

    Authors

    • Andrew Mitchell

      Univ Coll Dublin, Physics, University College Dublin

    • Jonas Rigo

      Physics, University College Dublin

    • Sudeshna Sen

      Physics, University College Dublin

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  • An assessment of the structural resolution of various fingerprints commonly used in machine learning

    ORAL

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    Presenters

    • Behnam Parsaeifard

      University of Basel

    Authors

    • Behnam Parsaeifard

      University of Basel

    • Deb De

      University of Basel

    • Anders Christensen

      University of Basel

    • Felix A Faber

      University of Basel

    • Emir Kocer

      goettingen university

    • Sandip De

      University of Basel

    • Jorg Behler

      Theoretische Chemie, Georg-August-Universität Göttingen, goettingen university, University of Göttingen

    • O. Von Lilienfeld

      University of Basel

    • Stefan A Goedecker

      Physics, University of Basel, University of Basel

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  • Vestigial nematic order in Pd-RTe3 studied using X-ray diffraction TEmperature Clustering (X-TEC)

    ORAL

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    Presenters

    • Eun-Ah Kim

      Cornell University, Department of Physics, Cornell University

    Authors

    • Krishnanand Mallayya

      Cornell University

    • Michael Matty

      Cornell University

    • Joshua Straquadine

      Stanford University

    • Matthew Krogstad

      Materials Science Division, Argonne National Laboratory, Argonne National Laboratory, Materials Science Division, Argonne National Lab, Material Science, Argonne National Laboratory, Material Science Division, Argonne National Laboratory

    • Raymond Osborn

      Materials Science Division, Argonne National Laboratory, Argonne National Laboratory, Materials Science Division, Argonne National Lab, Materials Science, Argonne National Laboratory, Material Science, Argonne National Laboratory, Material Science Division, Argonne National Laboratory

    • Stephan Rosenkranz

      Materials Science Division, Argonne National Laboratory, Argonne National Laboratory, Materials Science Division, Argonne National Lab, Materials Science, Argonne National Laboratory, Material Science, Argonne National Laboratory, Material Science Division, Argonne National Laboratory

    • Ian R Fisher

      Geballe Laboratory for Advanced Materials, Stanford University, Stanford Univ, Stanford University, Department of Applied Physics, Stanford University

    • Eun-Ah Kim

      Cornell University, Department of Physics, Cornell University

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  • Achieving Smaller Effective Spot Sizes in nano-ARPES with Machine Learning

    ORAL

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    Presenters

    • Conrad Stansbury

      University of California, Berkeley

    Authors

    • Conrad Stansbury

      University of California, Berkeley

    • Alessandra Lanzara

      University of California, Berkeley, Department of Physics, University of California, Physics, University of California, Berkeley, Lawrence Berkeley National Laboratory, Department of Physics, University of California Berkeley, Physics, University of California Berkeley, Physics, UC Berkeley

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  • INVESTIGATING BAND GAP DIRECTNESS USING MACHINE LEARNING

    ORAL

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    Presenters

    • Elton Ogoshi de Melo

      Center for Natural and Human Sciences, Federal University of ABC

    Authors

    • Elton Ogoshi de Melo

      Center for Natural and Human Sciences, Federal University of ABC

    • Mário Popolin Neto

      Institute of Mathematics and Computer Sciences, University of São Paulo

    • Carlos Mera Acosta

      Univ Federal do ABC, Renewable and Sustainable Energy Institute, University of Colorado, Boulder, Colorado 80309, USA, RASEI, University of Colorado, Boulder, Center for Natural and Human Sciences, Federal University of ABC

    • Gabriel M. Nascimento

      Center for Natural and Human Sciences, Federal University of ABC

    • João Rodrigues

      Center for Natural and Human Sciences, Federal University of ABC

    • Osvaldo N. Oliveira Jr.

      São Carlos Institute of Physics, University of São Paulo

    • Fernando V. Paulovich

      Faculty of Computer Science, Dalhousie University

    • Gustavo M. Dalpian

      Center for Natural and Human Sciences, Federal University of ABC

    View abstract →