Emerging Trends in Molecular Dynamics Simulations and Machine Learning III

Presentations

• Modeling Earth's interior from atomic to global scale

  ORAL · Invited

  March 16, 2022, 11:30 AM – March 16, 2022, 12:06 PM

  Publication: 1) R. M. Wentzcovitch et al., DOI: 10.1073/pnas.081215010
  2) G. Shephard et al., DOI: 10.1038/s41467-021-26115-z

  Presenters

  • Renata M Wentzcovitch

    • Columbia Univ
    • Columbia University

  Authors

  • Renata M Wentzcovitch

    • Columbia Univ
    • Columbia University

  View abstract →

• Molecular Dynamics Simulations of Solid Electrolytes with NequIP Equivariant Machine Learning Models

  ORAL

  March 16, 2022, 12:06 PM – March 16, 2022, 12:18 PM

  Presenters

  • Juan F Gomez

    • Harvard University

  Authors

  • Juan F Gomez

    • Harvard University

  • Liwen Wan

    • Lawrence Livermore National Lab

  • Simon L Batzner

    • Harvard University

  • Albert Musaelian

    • Harvard University

  • Brandon Wood

    • Lawrence Berkeley National Laboratory

  • Boris Kozinsky

    • Harvard University

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• Obtaining vibronic excitation spectra of small organic molecules using machine learning simulations and power spectra techniques

  ORAL

  March 16, 2022, 12:18 PM – March 16, 2022, 12:30 PM

  Presenters

  • Andrew M Johannesen

    • University of Minnesota

  Authors

  • Andrew M Johannesen

    • University of Minnesota

  • Jason D Goodpaster

    • University of Minnesota

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• Improved, Reliable Uncertainty Quantification of Interatomic Models using Sloppy Model Analysis

  ORAL

  March 16, 2022, 12:30 PM – March 16, 2022, 12:42 PM

  Presenters

  • Yonatan Kurniawan

    • Brigham Young University

  Authors

  • Yonatan Kurniawan

    • Brigham Young University

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• Large-scale dynamics simulations of complex liquid electrolytes with NequIP equivariant machine learning models.

  ORAL

  March 16, 2022, 12:42 PM – March 16, 2022, 12:54 PM

  Publication: [1] Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J.P., Kornbluth, M., Molinari, N., Smidt, T.E. and Kozinsky, B., 2021. Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. arXiv preprint arXiv:2101.03164.
  [2] Molinari, N., Mailoa, J.P. and Kozinsky, B., 2019. General trend of a negative Li effective charge in ionic liquid electrolytes. The journal of physical chemistry letters, 10(10), pp.2313-2319.

  Presenters

  • Nicola Molinari

    • Harvard University
    • Robert Bosch LLC Research and Technology Center North America; Harvard University

  Authors

  • Nicola Molinari

    • Harvard University
    • Robert Bosch LLC Research and Technology Center North America; Harvard University

  • Albert Musaelian

    • Harvard University

  • Simon L Batzner

    • Harvard University

  • Boris Kozinsky

    • Harvard University

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• Application of Machine Learning to the Development of Ti Interatomic Potentials

  ORAL

  March 16, 2022, 12:54 PM – March 16, 2022, 1:06 PM

  Publication: N/A

  Presenters

  • Sean J O'Connor

    • Honeywell FM&T

  Authors

  • Sean J O'Connor

    • Honeywell FM&T

  • Volker Eyert

    • Materials Design, Inc

  • Jörg-Rüdiger Hill

    • Materials Design, Inc

  • David Reith

    • Materials Design, Inc

  • Erich Wimmer

    • Materials Design, Inc

  • Patrick R Thomas

    • Honeywell FM&T
    • Department of Energy's Kansas City National Security Campus Managed by Honeywell FM&T

  • Ben Sikora

    • Honeywell FM&T
    • Department of Energy's Kansas City National Security Campus Managed by Honeywell FM&T

  • Paul Rulis

    • University of Missouri - Kansas City

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• Artifactual Liquid-Liquid Hydrogen Phase Transition from a Machine-Learnt Potential

  ORAL

  March 16, 2022, 1:06 PM – March 16, 2022, 1:18 PM

  Publication: Nature "Matters Arising", in press

  Presenters

  • Samuel B Trickey

    • University of Florida

  Authors

  • Samuel B Trickey

    • University of Florida

  • Valentin Karasiev

    • University of Rochester

  • Joshua Hinz

    • University of Rochester

  • Suxing Hu

    • Laboratory for Laser Energetics, University of Rochester

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• Machine Learned Interatomic Potential Development for W-ZrC

  ORAL

  March 16, 2022, 1:18 PM – March 16, 2022, 1:30 PM

  Presenters

  • Ember L Sikorski

    • Sandia National Laboratories

  Authors

  • Ember L Sikorski

    • Sandia National Laboratories

  • Julien Tranchida

    • CEA Cadarache
    • CEA

  • Mary Alice Cusentino

    • Sandia National Laboratories

  • Mitchell A Wood

    • Sandia National Laboratories

  • Aidan P Thompson

    • Sandia National Laboratories

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• Deep Potential Development of Highly Concentrated/High Entropy-driven Carbides

  ORAL

  March 16, 2022, 1:30 PM – March 16, 2022, 1:42 PM

  Presenters

  • Tyler J McGilvry-James

    • Missouri State University

  Authors

  • Tyler J McGilvry-James

    • Missouri State University

  • Marium Mostafiz Mou

    • Missouri State University

  • Ridwan Sakidja

    • Missouri State University
    • Physics, Astronomy and Materials Science, Missouri State University

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• Generative Coarse-Graining

  ORAL

  March 16, 2022, 1:42 PM – March 16, 2022, 1:54 PM

  Presenters

  • Wujie Wang

    • Massachusetts Institute of Technology MI

  Authors

  • Wujie Wang

    • Massachusetts Institute of Technology MI

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• Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

  ORAL

  March 16, 2022, 1:54 PM – March 16, 2022, 2:06 PM

  Publication: - H.E. Sauceda, M. Gastegger, S. Chmiela, K.-R. Müller, A. Tkatchenko "Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields" Journal of Chemical Physics 153 (12), 124109 (2020)
  - H.E. Sauceda, V. Vassilev-Galindo, S. Chmiela, K.-R. Müller, A. Tkatchenko "Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature" Nature Communications 12 (1), 1-10 (2021)
  - H.E. Sauceda, L.E. Gálvez-González, S. Chmiela, L.O. Paz-Borbón, K.-R. Müller, A. Tkatchenko "BIGDML: Towards Exact Machine Learning Force Fields for Materials" arXiv:2106.04229 (2021)

  Presenters

  • Huziel E Sauceda

    • Technical University of Berlin

  Authors

  • Huziel E Sauceda

    • Technical University of Berlin

  • Valentin Vassilev Galindo

    • University of Luxembourg Limpertsberg

  • Stefan Chmiela

    • Tech Univ Berlin

  • Klaus-Robert Müller

    • Technical University of Berlin

  • Alexandre Tkatchenko

    • University of Luxembourg Limpertsberg

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• Quantum paraelectricity and structural phase transitions of SrTiO₃ by on-the-fly machine-learned interatomic potentials

  ORAL

  March 16, 2022, 2:06 PM – March 16, 2022, 2:18 PM

  Presenters

  • Carla Verdi

    • Univ of Vienna
    • University of Vienna

  Authors

  • Carla Verdi

    • Univ of Vienna
    • University of Vienna

  • Luigi Ranalli

    • Univ of Vienna

  • Cesare Franchini

    • University of Vienna
    • Univ of Vienna
    • Univ of Vienna, Univ of Bologna
    • Universita' di Bologna & University of Vienna
    • University of Vienna, A-1090 Vienna, Austria, Alma Mater Studiorum–Università di Bologna, Bologna, 40127, Italy

  • Georg Kresse

    • Univ of Vienna
    • University of Vienna

  View abstract →