Emerging Trends in Molecular Dynamics Simulations and Data Analytics I

FOCUS · K21

Presentations

  • Reactive molecular dynamics simulations and machine learning

    Invited

    –

    Presenters

    • Priya Vashishta

      University of Southern California, Physics, University of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California, Physics & Astronomy, University of Southern California

    Authors

    • Priya Vashishta

      University of Southern California, Physics, University of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California, Physics & Astronomy, University of Southern California

    View abstract →

  • Nanoindentation on Monolayer Kirigami MoS<sub>2</sub>

    ORAL

    –

    Presenters

    • Beibei Wang

      University of Southern California

    Authors

    • Beibei Wang

      University of Southern California

    • Rajiv Kalia

      University of Southern California, Physics, University of Southern California, Physics & Astronomy, University of Southern California

    • Aiichiro Nakano

      University of Southern California, Physics, University of Southern California, Physics & Astronomy, University of Southern California

    • Priya Vashishta

      University of Southern California, Physics, University of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California, Physics & Astronomy, University of Southern California

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  • Theoretical Studies of Water by Climbing Jacob’s Ladder with Deep Learning

    ORAL

    –

    Presenters

    • Mohan Chen

      Temple University

    Authors

    • Mohan Chen

      Temple University

    • Linfeng Zhang

      Princeton University

    • Han Wang

      Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics

    • Jianhang Xu

      Temple University

    • Hsin-Yu Ko

      Princeton University, Chemistry, Princeton University

    • Biswajit Santra

      Temple University, Physics, Temple University

    • John P Perdew

      Temple University, Physics, Temple University

    • Weinan E

      Princeton University

    • Xifan Wu

      Temple University, Physics, Temple University

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  • Machine Learning Polarizable Force Field Parameters

    ORAL

    –

    Presenters

    • Ying Li

      Argonne National Laboratory

    Authors

    • Ying Li

      Argonne National Laboratory

    • Hui Li

      University of Chicago

    • Frank Pickard

      National Institutes of Health

    • Badri Narayanan

      Argonne National Laboratory

    • Subramanian Sankaranarayanan

      Argonne National Laboratory

    • Maria Chan

      Argonne National Lab, Argonne National Laboratory, Center for Nanoscale Materials, Argonne National Laboratory

    • Benard Brooks

      National Institutes of Health

    • Benoit Roux

      University of Chicago

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  • Hot spot formation and shock initiation of RDX

    ORAL

    –

    Presenters

    • Ankit Mishra

      University of Southern California

    Authors

    • Ankit Mishra

      University of Southern California

    • Ken-ichi Nomura

      University of Southern California

    • Aiichiro Nakano

      University of Southern California, Physics, University of Southern California, Physics & Astronomy, University of Southern California

    • Rajiv Kalia

      University of Southern California, Physics, University of Southern California, Physics & Astronomy, University of Southern California

    • Priya Vashishta

      University of Southern California, Physics, University of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California, Physics & Astronomy, University of Southern California

    View abstract →

  • Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

    ORAL

    –

    Presenters

    • Stefan Chmiela

      Machine Learning/Intelligent Data Analysis, Technische Universität Berlin

    Authors

    • Stefan Chmiela

      Machine Learning/Intelligent Data Analysis, Technische Universität Berlin

    • Huziel Sauceda

      Theory Department, Fritz Haber Institute of the MPG, Theory Department, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Technical University of Berlin

    • Klaus-Robert Müller

      Machine Learning Group, Technische Universität Berlin, Technical University of Berlin, Machine Learning/Intelligent Data Analysis, Technische Universität Berlin

    • Alexandre Tkatchenko

      University of Luxembourg, FSTC, University of Luxembourg, Physics and Materials Science Research Unit, University of Luxembourg, Physics and Materials Science Reasearch Unit, University of Luxembourg, Physics and Materials Science Research Unit, Université du Luxembourg

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  • Constructing Accurate Machine Learning Force Fields for Flexible Molecules

    ORAL

    –

    Presenters

    • Valentin Vassilev Galindo

      Physics and Materials Science Reasearch Unit, University of Luxembourg

    Authors

    • Valentin Vassilev Galindo

      Physics and Materials Science Reasearch Unit, University of Luxembourg

    • Igor Poltavskyi

      FSTC, University of Luxembourg, Physics and Materials Science Reasearch Unit, University of Luxembourg

    • Alexandre Tkatchenko

      University of Luxembourg, FSTC, University of Luxembourg, Physics and Materials Science Research Unit, University of Luxembourg, Physics and Materials Science Reasearch Unit, University of Luxembourg, Physics and Materials Science Research Unit, Université du Luxembourg

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  • Machine Learning for Auto-tuning of Simulation Parameters in Car-Parrinello Molecular Dynamics

    ORAL

    –

    Presenters

    • Jayanath Chamindu Kadupitige

      Intelligent Systems Engineering, Indiana University Bloomington

    Authors

    • Jayanath Chamindu Kadupitige

      Intelligent Systems Engineering, Indiana University Bloomington

    • Geoffrey C Fox

      Intelligent Systems Engineering, Indiana University Bloomington

    • Vikram Jadhao

      Intelligent Systems Engineering, Indiana University Bloomington

    View abstract →