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, 12:30 PM – March 16, 2022, 1:06 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, 1:06 PM – March 16, 2022, 1:18 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

  View abstract →

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

  ORAL

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

  Presenters

  • Andrew M Johannesen

    University of Minnesota

  Authors

  • Andrew M Johannesen

    University of Minnesota

  • Jason D Goodpaster

    University of Minnesota

  View abstract →

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

  ORAL

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

  Presenters

  • Yonatan Kurniawan

    Brigham Young University

  Authors

  • Yonatan Kurniawan

    Brigham Young University

  View abstract →

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

  ORAL

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

  View abstract →

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

  ORAL

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

  View abstract →

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

  ORAL

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

  View abstract →

• Machine Learned Interatomic Potential Development for W-ZrC

  ORAL

  March 16, 2022, 2:18 PM – March 16, 2022, 2:30 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

  View abstract →

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

  ORAL

  March 16, 2022, 2:30 PM – March 16, 2022, 2:42 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

  View abstract →

• Generative Coarse-Graining

  ORAL

  March 16, 2022, 2:42 PM – March 16, 2022, 2:54 PM

  Presenters

  • Wujie Wang

    Massachusetts Institute of Technology MI

  Authors

  • Wujie Wang

    Massachusetts Institute of Technology MI

  View abstract →

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

  ORAL

  March 16, 2022, 2:54 PM – March 16, 2022, 3:06 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

  View abstract →

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

  ORAL

  March 16, 2022, 3:06 PM – March 16, 2022, 3:18 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 →