AI and ML in Monte Carlo and molecular dynamics simulations

Presentations

• Free Energy, Conformationa Dynamics and Simulations of Nanocrystals with Explicit Ligands

  ORAL · Invited

  March 4, 2024, 3:00 PM – March 4, 2024, 3:36 PM

  Publication: None

  Presenters

  • Alex Travesset

    • Iowa State University and Ames National Laboratory
    • Ames Lab

  Authors

  • Alex Travesset

    • Iowa State University and Ames National Laboratory
    • Ames Lab

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• Evaluating approaches for on-the-fly machine-learning interatomic potentials for activated mechanisms sampling with ARTn

  ORAL

  March 4, 2024, 3:36 PM – March 4, 2024, 3:48 PM

  Publication: Eugène Sanscartier, Félix Saint-Denis, Karl-Étienne Bolduc, Normand Mousseau; Evaluating approaches for on-the-fly machine learning interatomic potentials for activated mechanisms sampling with the activation-relaxation technique nouveau. J. Chem. Phys. 28 June 2023; 158 (24): 244110. https://doi.org/10.1063/5.0143211

  Presenters

  • Eugene Sanscartier

    • Universite de Montreal

  Authors

  • Eugene Sanscartier

    • Universite de Montreal

  • Normand Mousseau

    • Universite de Montreal

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• Development of MLIP to model corrosion behavior in Molten Salt Reactors

  ORAL

  March 4, 2024, 3:48 PM – March 4, 2024, 4:00 PM

  Presenters

  • Matthew D Bruenning

    • Missouri State University

  Authors

  • Matthew D Bruenning

    • Missouri State University

  • Ridwan Sakidja

    • Missouri State University

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• Machine learning assisted design of effective potentials, surface ligand patterns, and annealing protocols for colloidal self-assembly

  ORAL · Invited

  March 4, 2024, 4:00 PM – March 4, 2024, 4:36 PM

  Presenters

  • Gaurav Arya

    • Duke University

  Authors

  • Gaurav Arya

    • Duke University

  • Yilong Zhou

    • Duke University

  • Po-An Lin

    • Duke University

  • Safak Callioglu

    • Duke University

  • Sigbjorn L Bore

    • UC San Diego

  • Simiao Ren

    • Duke University

  • Yunqi Yang

    • Duke University

  • Andrea Tao

    • UC San Diego

  • Leslie Collins

    • Duke University

  • Francesco Paesani

    • University of California, San Diego

  • Yonggang Ke

    • Duke University

  • Stefan Zauscher

    • Duke University

  View abstract →

• How to use stochastic devices in probabilistic calculations

  ORAL

  March 4, 2024, 4:36 PM – March 4, 2024, 4:48 PM

  Presenters

  • Shashank Misra

    • Sandia National Laboratories

  Authors

  • Shashank Misra

    • Sandia National Laboratories

  • Christopher R Allemang

    • Sandia National Laboratories

  • Christopher D Arose

    • Sandia National Laboratories

  • Brady G Taylor

    • Duke University

  • Andre Dubovskiy

    • New York University, Department of Physics
    • New York University (NYU)

  • Ahmed Sidi El Valli

    • New York University, Department of Physics
    • New York University (NYU)

  • Laura Rehm

    • New York University, Department of Physics
    • New York University (NYU)

  • Andrew Haas

    • New York University, Department of Physics
    • New York University (NYU)

  • Andrew D Kent

    • New York University, Department of Physics
    • Department of Physics, New York University
    • New York University

  • Leslie C Bland

    • Temple University

  • Suma G Cardwell

    • Sandia National Laboratories

  • Darby Smith

    • Sandia National Laboratories

  • James B Aimone

    • Sandia National Laboratories

  View abstract →

• First-principles machine-learning quantum dynamics at 0K in SrTiO₃: light-induced ultrafast ferroelectric transition

  ORAL

  March 4, 2024, 4:48 PM – March 4, 2024, 5:00 PM

  Presenters

  • Francesco Libbi

    • Harvard University

  Authors

  • Francesco Libbi

    • Harvard University

  • Lorenzo Monacelli

    • Ecole Politecnique Federal de Lausanne

  • Anders Johansson

    • Harvard University

  • Boris Kozinsky

    • Harvard University

  View abstract →

• Diverse training data generation for machine-learning interatomic potentials

  ORAL

  March 4, 2024, 5:00 PM – March 4, 2024, 5:12 PM

  Presenters

  • Aparna P. A. Subramanyam

    • Los Alamos National Laboratory

  Authors

  • Aparna P. A. Subramanyam

    • Los Alamos National Laboratory

  • Danny Perez

    • Los Alamos Natl Lab
    • Los Alamos National Laboratory

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• A machine learning interatomic potential for Ge-Te alloys

  ORAL

  March 4, 2024, 5:12 PM – March 4, 2024, 5:24 PM

  Presenters

  • Tom Arbaugh

    • Wesleyan University

  Authors

  • Tom Arbaugh

    • Wesleyan University

  • Owen Dunton

    • Wesleyan University

  • Francis W Starr

    • Wesleyan University

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• Machine learned force and torque predictions for molecular dynamics of non-spherical colloids

  ORAL

  March 4, 2024, 5:24 PM – March 4, 2024, 5:36 PM

  Presenters

  • Bahadir Rusen Argun

    • University of Illinois at Urbana-Champaign

  Authors

  • Bahadir Rusen Argun

    • University of Illinois at Urbana-Champaign

  • Antonia Statt

    • University of Illinois at Urbana-Champaign

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• Machine Learning Models for Partition Functions: Predicting Thermodynamic Properties and Exploring Transition Pathways

  ORAL

  March 4, 2024, 5:36 PM – March 4, 2024, 5:48 PM

  Presenters

  • Caroline Desgranges

    • University of Massachusetts Lowell

  Authors

  • Caroline Desgranges

    • University of Massachusetts Lowell

  • Jerome Delhommelle

    • University of Massachusetts, Lowell

  View abstract →