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Emerging Trends in Molecular Dynamics Simulations and Machine Learning I

FOCUS · S46 · ID: 47202

Presentations

  • Deep potential molecular dynamics of water self-ionization

    ORAL

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    Presenters

    • Marcos F Calegari Andrade

      Princeton University

    Authors

    • Marcos F Calegari Andrade

      Princeton University

    • Roberto Car

      Princeton University

    • Annabella Selloni

      Princeton University, Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA

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  • Dynamics of Wrinkle-Ridge Transition in Graphene Supported on a Polymer: Quantum Molecular Dynamics Simulations

    ORAL

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    Presenters

    • Anikeya Aditya

      University of Southern California

    Authors

    • Anikeya Aditya

      University of Southern California

    • Shogo Fukushima

      Kumamoto University, University of Southern California, Univ of Southern California

    • Ankit Mishra

      Univ of Southern California

    • Ken-ichi Nomura

      University of Southern California, Univ of Southern California, University Of Southern California

    • Fuyuki Shimojo

      Kumamoto Univ

    • Aiichiro Nakano

      Univ of Southern California

    • Priya Vashishta

      Univ of Southern California, University of Southern California

    • Rajiv K Kalia

      Univ of Southern California

    • Mark J Stevens

      Sandia National Laboratories

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  • Many-body interatomic potential with Bayesian active learning, an application ofSiC

    ORAL

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    Publication: [1] Vandermause, J., Torrisi, S.B., Batzner, S., Xie, Y., Sun, L., Kolpak, A.M. and Kozinsky, B., 2020. On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events. npj Computational Materials, 6(1), pp.1-11.<br>[2] Xie, Y., Vandermause, J., Sun, L., Cepellotti, A. and Kozinsky, B., 2021. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene. npj Computational Materials, 7(1), pp.1-10.<br>[3] Vandermause, J., Xie, Y., Lim, J.S., Owen, C.J. and Kozinsky, B., 2021. Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics. arXiv preprint arXiv:2106.01949.<br>[4] Ramakers, S.J.J., Eckl, T., Marusczyk, A., Hammerschmidt, T., Mrovec, M., Drautz, R. Effects of thermal, elastic and surface properties on the polytype stability of SiC: an ab initio study including van der Waals interactions. In preparation.<br>[5] Xie, Y., Vandermause, J., Ramakers, S., Protik, N. H., Johansson, A., and Kozinsky, B. On-the-fly Bayesian Learning with LAMMPS Molecular Dynamics, an Application of Many-body Potential of SiC. In preparation.

    Presenters

    • Yu Xie

      Harvard University

    Authors

    • Yu Xie

      Harvard University

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  • Opto-Electro-Mechanical control of Ferroelectric Topological Structures for Ultralow Power Topotronic Devices using Hybrid Neural Network Quantum Molecular Dynamics and Molecular Mechanics Simulations

    ORAL

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    Presenters

    • Thomas M Linker

      University of Southern California

    Authors

    • Thomas M Linker

      University of Southern California

    • Ken-ichi Nomura

      University of Southern California, Univ of Southern California, University Of Southern California

    • Shogo Fukushima

      Kumamoto University, University of Southern California, Univ of Southern California

    • Rajiv K Kalia

      Univ of Southern California

    • Aravind Krishnamoorthy

      Univ of Southern California, University of Southern California

    • Aiichiro Nakano

      Univ of Southern California

    • Kohei Shimamura

      Kumamoto University, Kumamoto Univ

    • Fuyuki Shimojo

      Kumamoto Univ

    • Priya Vashishta

      Univ of Southern California, University of Southern California

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  • Designing Machine Learning Surrogates using Outputs of Molecular Dynamics Simulations as Soft Labels

    ORAL

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    Publication: Kadupitiya, JCS; Sun, Fanbo; Fox, Geoffrey; Jadhao, Vikram, Machine learning surrogates for molecular dynamics simulations of soft materials, Journal of Computational Science,42,101107,2020, Elsevier<br>Kadupitiya, JCS; Fox, Geoffrey C; Jadhao, Vikram, Machine learning for performance enhancement of molecular dynamics simulations, International Conference on Computational Science,116-130,2019, Springer

    Presenters

    • Jayanath Chamindu Sandanuwan K Kadupitige

      Indiana University Bloomington

    Authors

    • Jayanath Chamindu Sandanuwan K Kadupitige

      Indiana University Bloomington

    • Nasim Anousheh

      Indiana University Bloomington

    • Vikram Jadhao

      Indiana University Bloomington

    View abstract →