Emerging Trends in Molecular Dynamics Simulations and Machine Learning I
FOCUS · S46 · ID: 47202
Presentations
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AI-driven modeling of quantum materials architectures
ORAL · Invited
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Presenters
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Rajiv K Kalia
Univ of Southern California
Authors
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Rajiv K Kalia
Univ of Southern California
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Deep potential molecular dynamics of water self-ionization
ORAL
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Presenters
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Marcos F Calegari Andrade
Princeton University
Authors
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Marcos F Calegari Andrade
Princeton University
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Roberto Car
Princeton University
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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
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Anikeya Aditya
University of Southern California
Authors
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Anikeya Aditya
University of Southern California
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Shogo Fukushima
Kumamoto University, University of Southern California, Univ of Southern California
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Ankit Mishra
Univ of Southern California
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Ken-ichi Nomura
University of Southern California, Univ of Southern California, University Of Southern California
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Fuyuki Shimojo
Kumamoto Univ
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Aiichiro Nakano
Univ of Southern California
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Priya Vashishta
Univ of Southern California, University of Southern California
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Rajiv K Kalia
Univ of Southern California
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Mark J Stevens
Sandia National Laboratories
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A Critical Assessment of Neural Network Potentials for Water and the Role of Nuclear Quantum Effects through the Van Hove Correlation Function
ORAL
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Presenters
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Murali Gopal Muraleedharan
Oak Ridge National Laboratory
Authors
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Murali Gopal Muraleedharan
Oak Ridge National Laboratory
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Paul Kent
Oak Ridge National Lab, Oak Ridge National Laboratory
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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.
[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.
[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.
[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.
[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
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Yu Xie
Harvard University
Authors
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Yu Xie
Harvard University
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Structural optimization using learned optimizers and graph neural networks
ORAL · Invited
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Presenters
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Ekin D Cubuk
Google LLC
Authors
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Ekin D Cubuk
Google LLC
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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
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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, University Of Southern California
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Shogo Fukushima
Kumamoto University, University of Southern California, Univ of Southern California
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Rajiv K Kalia
Univ of Southern California
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Aravind Krishnamoorthy
Univ of Southern California, University of Southern California
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Aiichiro Nakano
Univ of Southern California
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Kohei Shimamura
Kumamoto University, Kumamoto Univ
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Fuyuki Shimojo
Kumamoto Univ
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Priya Vashishta
Univ of Southern California, University of Southern California
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Active learning of reactive Bayesian force fields: Application to heterogeneous catalysis dynamics of H/Pt
ORAL
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Publication: https://arxiv.org/abs/2106.01949
Presenters
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Jonathan P Vandermause
Harvard University
Authors
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Jonathan P Vandermause
Harvard University
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Yu Xie
Harvard University
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Jin Soo Lim
Harvard University
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Cameron J Owen
Harvard University
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Boris Kozinsky
Harvard University
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Committee Disagreement Biased Active Learning of Interatomic Potentials
ORAL
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Presenters
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Michael J Waters
Northwestern University
Authors
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Michael J Waters
Northwestern University
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James M Rondinelli
Northwestern University
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Ab Initio Thermodynamics of Ferroelectrics: The case of PbTiO3
ORAL
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Presenters
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Pinchen Xie
Princeton University
Authors
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Pinchen Xie
Princeton University
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Yixiao Chen
Princeton University
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Weinan E
Princeton University
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Roberto Car
Princeton University
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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
Kadupitiya, JCS; Fox, Geoffrey C; Jadhao, Vikram, Machine learning for performance enhancement of molecular dynamics simulations, International Conference on Computational Science,116-130,2019, SpringerPresenters
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Jayanath Chamindu Sandanuwan K Kadupitige
Indiana University Bloomington
Authors
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Jayanath Chamindu Sandanuwan K Kadupitige
Indiana University Bloomington
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Nasim Anousheh
Indiana University Bloomington
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Vikram Jadhao
Indiana University Bloomington
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