Emerging Trends in Molecular Dynamics Simulations and Machine Learning I
FOCUS · D60 · ID: 1067982
Presentations
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Exascale Simulations of Quantum Materials Guided by AI and Quantum Computing
ORAL · Invited
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Presenters
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Aiichiro Nakano
University of Southern California
Authors
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Aiichiro Nakano
University of Southern California
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Large scale simulations of soft materials with equivariant deep learning potentials
ORAL
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Presenters
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Simon L Batzner
Harvard University
Authors
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Simon L Batzner
Harvard University
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Albert Musaelian
Harvard University
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Boris Kozinsky
Harvard University
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Multi-Scale Neural Network Molecular Dynamics Simulations for Polar Topology Control in Next Generation Ferroelectric Materials.
ORAL
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Presenters
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Thomas M Linker
University of Southern California
Authors
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Thomas M Linker
University of Southern California
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Ken-ichi Nomura
University of Southern California, Univ of Southern California
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Shogo Fukushima
University of Southern California
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Rajiv K Kalia
Univ of Southern California
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Aravind Krishnamoorthy
University of Southern California
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Aiichiro Nakano
University of Southern California
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Kohei Shimamura
Kumamoto University
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Fuyuki Shimojo
Kumamoto University, Kumamoto Univ
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Priya Vashishta
University of Southern California
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Learning the committor probability using data-driven path collective variables
ORAL
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Presenters
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Arthur France-Lanord
CNRS-IMPMC, CNRS - IMPMC
Authors
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Arthur France-Lanord
CNRS-IMPMC, CNRS - IMPMC
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Hadrien Vroylandt
Sorbonne Université - ISCD
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Fabio Pietrucci
Sorbonne université-IMPMC, Sorbonne Université - IMPMC
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Benjamin Rotenberg
CNRS - PHENIX
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A. Marco Saitta
Sorbonne université-IMPMC, Sorbonne University, Sorbonne Université - IMPMC
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Mathieu Salanne
Sorbonne Université - PHENIX
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Billions of Atoms with Machine Learning Interatomic Potentials: Application to Direct Heterogeneous Reactive Dynamics
ORAL
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Publication: arXiv:2204.12573
Presenters
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Anders Johansson
Harvard University
Authors
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Anders Johansson
Harvard University
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Yu Xie
Harvard University
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Cameron J Owen
Harvard University
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Jin Soo Lim
Harvard University
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Lixin Sun
Harvard University
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Jonathan P Vandermause
Harvard University
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Boris Kozinsky
Harvard University
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Machine learning based force-fields for strongly anharmonic materials
ORAL
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Presenters
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Mei-Yin Chou
Academia Sinica, Institute of Atomic and Molecular Sciences, Academia Sinica
Authors
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Martin Callsen
Institute of Atomic and Molecular Sciences, Academia Sinica
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Mei-Yin Chou
Academia Sinica, Institute of Atomic and Molecular Sciences, Academia Sinica
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DFT aided machine learning interatomic potentials for realistic simulations of low dimensional system
ORAL · Invited
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Presenters
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Duy Le
Univeristy of Central Florida, Department of Physics, University of Central Florida
Authors
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Duy Le
Univeristy of Central Florida, Department of Physics, University of Central Florida
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Decoding the Hydrogen Bond Network of Water in Carbon Nanotubes with Atomistic Simulations
ORAL
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Presenters
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Marcos Calegari Andrade
Lawrence Livermore National Lab
Authors
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Marcos Calegari Andrade
Lawrence Livermore National Lab
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Tuan Anh Pham
Lawrence Livermore Natl Lab
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Kibble-Zurek Scaling Study of Phase Transition in Barium Titanate (BaTiO<sub>3</sub>)
ORAL
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Presenters
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Nitish Baradwaj
University of Southern California
Authors
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Nitish Baradwaj
University of Southern California
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Aravind Krishnamoorthy
University of Southern California
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Ken-ichi Nomura
University of Southern California, Univ of Southern California
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Aiichiro Nakano
University of Southern California
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Rajiv K Kalia
Univ of Southern California
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Priya Vashishta
University of Southern California
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Indicator configuration: An information-matching method of data reduction for training interatomic potential
ORAL
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Presenters
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Yonatan Kurniawan
Brigham Young University
Authors
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Yonatan Kurniawan
Brigham Young University
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Mark K Transtrum
Brigham Young University
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Cody L Petrie
Brigham Young University
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Dylan B Bailey
Brigham Young University
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