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Data Science II: Machine Learning

ORAL · G20 · ID: 355166

Presentations

  • Addressing the Elephant in the Room: Uncertainties in Physical Predictions From Machine-Learned Force Fields

    ORAL

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    Presenters

    • Stefan Chmiela

      Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin

    Authors

    • Stefan Chmiela

      Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin

    • Huziel Sauceda

      Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin

    • Klaus-Robert Müller

      Tech Univ Berlin, Machine Learning Group, Technische Universität Berlin

    • Alexandre Tkatchenko

      Physics and Materials Science Reasearch Unit, University of Luxembourg, Physics and Materials Science Research Unit, University of Luxembourg, University of Luxembourg, University of Luxembourg Limpertsberg

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  • Machine learning on the electron-phonon spectral function and the superconductor gap function

    ORAL

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    Presenters

    • Ming-Chien Hsu

      Physics, Natl Sun Yat Sen Univ

    Authors

    • Ming-Chien Hsu

      Physics, Natl Sun Yat Sen Univ

    • Wan-Ju Li

      Physics, Natl Sun Yat Sen Univ

    • Ting-Kuo Lee

      Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan, Natl Sun Yat Sen Univ, Physics, Natl Sun Yat Sen Univ

    • Shin-Ming Huang

      National Sun Yat-sen University, Natl Sun Yat Sen Univ, Physics, Natl Sun Yat Sen Univ, National Sun Yat-Sen University

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  • Characteristic space of XRD patterns in machine-learning

    ORAL

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    Presenters

    • Keishu Uchimura

      School of Materials Science, JAIST, Japan Adv Inst of Sci and Tech

    Authors

    • Keishu Uchimura

      School of Materials Science, JAIST, Japan Adv Inst of Sci and Tech

    • Masao Yano

      TOYOTA MOTOR CORPORATION

    • Hiroyuki Kimoto

      TOYOTA MOTOR CORPORATION

    • Kenta Hongo

      Research Center for Advanced Computing Infrastructure, JAIST, Japan Adv Inst of Sci and Tech

    • Ryo Maezono

      School of Information Science, JAIST, JAIST (Japan Advanced Institute of Science and Technology), Japan Adv Inst of Sci and Tech

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  • Machine Learning of Energetic Material Properties and Performance

    ORAL

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    Presenters

    • Brian Barnes

      Army Research Laboratory, Detonation Science and Modeling Branch, CCDC Army Research Laboratory, CCDC Army Research Laboratory, US Army Rsch Lab - Aberdeen

    Authors

    • Brian Barnes

      Army Research Laboratory, Detonation Science and Modeling Branch, CCDC Army Research Laboratory, CCDC Army Research Laboratory, US Army Rsch Lab - Aberdeen

    • Betsy M Rice

      CCDC Army Research Laboratory

    • Andrew E Sifain

      CCDC Army Research Laboratory

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  • Simulation of atmospheric turbulence with generative machine learning models

    ORAL

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    Presenters

    • Arturo Rodriguez

      University of Texas, El Paso

    Authors

    • Arturo Rodriguez

      University of Texas, El Paso

    • Carlos R Cuellar

      University of Texas, El Paso

    • Luis Fernando Rodriguez

      University of Texas, El Paso

    • Armando Garcia

      University of Texas, El Paso

    • Jose Terrazas

      University of Texas, El Paso

    • VM Krushnarao Kotteda

      The University of Wyoming

    • Rao Gudimetla

      Air Force Research Laboratory, Air Force Research Lab

    • Vinod Kumar

      University of Texas, El Paso

    • Jorge Munoz

      University of Texas, El Paso

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  • Identification of informative acoustic features in the transition from non-violent to violent crowd behavior

    ORAL

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    Presenters

    • Katrina Pedersen

      Brigham Young Univ - Provo

    Authors

    • Katrina Pedersen

      Brigham Young Univ - Provo

    • Brooks A Butler

      Brigham Young Univ - Provo

    • Sean Warnick

      Brigham Young Univ - Provo

    • Kent L Gee

      Brigham Young Univ - Provo

    • Mark Transtrum

      Brigham Young Univ - Provo, Physics & Astronomy, Brigham Young University, Brigham Young University, Physics and Astronomy, Brigham Young University

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  • Data Augmentation and Pre-training for Template-Based Retrosynthetic Prediction

    ORAL

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    Presenters

    • Mike Fortunato

      Department of Chemical Engineering, Massachusetts Institute of Technology

    Authors

    • Mike Fortunato

      Department of Chemical Engineering, Massachusetts Institute of Technology

    • Connor Coley

      Department of Chemical Engineering, Massachusetts Institute of Technology

    • Brian Barnes

      Army Research Laboratory, Detonation Science and Modeling Branch, CCDC Army Research Laboratory, CCDC Army Research Laboratory, US Army Rsch Lab - Aberdeen

    • Klavs Jensen

      Department of Chemical Engineering, Massachusetts Institute of Technology

    View abstract →