Emerging Trends in Molecular Dynamics Simulations and Data Analytics I
FOCUS · K21 ·
Presentations
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Reactive molecular dynamics simulations and machine learning
Invited
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Presenters
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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
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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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High-Dimensional Neural Network Potentials for Complex Systems
Invited
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Presenters
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Jörg Behler
- Chemistry, Göttingen University
Authors
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Jörg Behler
- Chemistry, Göttingen University
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Nanoindentation on Monolayer Kirigami MoS<sub>2</sub>
ORAL
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Presenters
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Beibei Wang
- University of Southern California
Authors
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Beibei Wang
- University of Southern California
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Rajiv Kalia
- University of Southern California
- Physics, University of Southern California
- Physics & Astronomy, University of Southern California
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Aiichiro Nakano
- University of Southern California
- Physics, University of Southern California
- Physics & Astronomy, University of Southern California
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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
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Presenters
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Mohan Chen
- Temple University
Authors
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Mohan Chen
- Temple University
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Linfeng Zhang
- Princeton University
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Han Wang
- Laboratory of Computational Physics
- Institute of Applied Physics and Computational Mathematics
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Jianhang Xu
- Temple University
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Hsin-Yu Ko
- Princeton University
- Chemistry, Princeton University
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Biswajit Santra
- Temple University
- Physics, Temple University
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John P Perdew
- Temple University
- Physics, Temple University
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Weinan E
- Princeton University
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Xifan Wu
- Temple University
- Physics, Temple University
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Machine Learning Polarizable Force Field Parameters
ORAL
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Presenters
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Ying Li
- Argonne National Laboratory
Authors
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Ying Li
- Argonne National Laboratory
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Hui Li
- University of Chicago
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Frank Pickard
- National Institutes of Health
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Badri Narayanan
- Argonne National Laboratory
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Subramanian Sankaranarayanan
- Argonne National Laboratory
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Maria Chan
- Argonne National Lab
- Argonne National Laboratory
- Center for Nanoscale Materials, Argonne National Laboratory
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Benard Brooks
- National Institutes of Health
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Benoit Roux
- University of Chicago
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Hot spot formation and shock initiation of RDX
ORAL
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Presenters
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Ankit Mishra
- University of Southern California
Authors
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Ankit Mishra
- University of Southern California
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Ken-ichi Nomura
- University of Southern California
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Aiichiro Nakano
- University of Southern California
- Physics, University of Southern California
- Physics & Astronomy, University of Southern California
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Rajiv Kalia
- University of Southern California
- Physics, University of Southern California
- Physics & Astronomy, University of Southern California
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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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Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
ORAL
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Presenters
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Stefan Chmiela
- Machine Learning/Intelligent Data Analysis, Technische Universität Berlin
Authors
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Stefan Chmiela
- Machine Learning/Intelligent Data Analysis, Technische Universität Berlin
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Huziel Sauceda
- Theory Department, Fritz Haber Institute of the MPG
- Theory Department, Fritz-Haber-Institut der Max-Planck-Gesellschaft
- Technical University of Berlin
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Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin
- Technical University of Berlin
- Machine Learning/Intelligent Data Analysis, Technische Universität Berlin
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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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Atomic Scale Response of Electrochemical Systems Under Potential Bias
ORAL
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Presenters
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James Goff
- Materials Science and Engineering, Pennsylvania State University
Authors
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James Goff
- Materials Science and Engineering, Pennsylvania State University
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Constructing Accurate Machine Learning Force Fields for Flexible Molecules
ORAL
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Presenters
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Valentin Vassilev Galindo
- Physics and Materials Science Reasearch Unit, University of Luxembourg
Authors
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Valentin Vassilev Galindo
- Physics and Materials Science Reasearch Unit, University of Luxembourg
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Igor Poltavskyi
- FSTC, University of Luxembourg
- Physics and Materials Science Reasearch Unit, University of Luxembourg
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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
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Presenters
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Jayanath Chamindu Kadupitige
- Intelligent Systems Engineering, Indiana University Bloomington
Authors
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Jayanath Chamindu Kadupitige
- Intelligent Systems Engineering, Indiana University Bloomington
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Geoffrey C Fox
- Intelligent Systems Engineering, Indiana University Bloomington
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Vikram Jadhao
- Intelligent Systems Engineering, Indiana University Bloomington
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Parallel Trajectory Splicing and the Value of Information
ORAL
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Presenters
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Andrew Garmon
- Physics, Clemson University
Authors
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Andrew Garmon
- Physics, Clemson University
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Danny Perez
- Los Alamos Nationl Lab
- Los Alamos National Laboratory
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