FMDA/B: Machine-Learned Interatomic Potentials

ORAL · W04 · ID: 3362504

Presentations

  • Progress Towards Quantum Accurate Atomistic Simulations of Shock Propagation and Release in DT

    ORAL

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    Publication: J. X. D'Souza, S.X. Hu, D. I. Mihaylov, V. V. Karasiev, V. N. Goncharov, and S. Zhang, "Designing a Quantum-Accurate Machine-Learning Potential to Enable Large-Scale Simulations of Deuterium Under Shock," Physics of Plasmas (2024) [submitted]

    Presenters

    • Justin X D'Souza

      University of Rochester

    Authors

    • Justin X D'Souza

      University of Rochester

    • Deyan I Mihaylov

      University of Rochester

    • Suxing Hu

      University of Rochester

    • Valentin V Karasiev

      University of Rochester

    • Valeri N Goncharov

      University of Rochester

    • Shuai Zhang

      University of Rochester, Laboratory for Laser Energetics, University of Rochester

    View abstract →

  • Supercritcal to Superionic: ACE'ing the response of water, hydrocarbons, and ammonia under shock conditions

    ORAL

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    Publication: (i) Atomic Cluster Expansion Potential for Large Scale Simulations of Hydrocarbons Under Shock Compression, J. Chem. Phys. 161, 064303 (2024)
    (ii) Accurate and efficient parameterization of an atomic cluster expansion (ACE) potential for ammonia under extreme conditions

    Presenters

    • Jonathan T Willman

      Los Alamos National Laboratory

    Authors

    • Jonathan T Willman

      Los Alamos National Laboratory

    • Romain Perriot

      Theoretical Division, Los Alamos National Laboratory

    • Christopher C Ticknor

      Los Alamos National Laboratory (LANL)

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  • Atomistic modelling of the orientation dependence of shock-induced phase transitions in tin

    ORAL

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    Presenters

    • Marti Puig Fantauzzi

      University of Oxford

    Authors

    • Marti Puig Fantauzzi

      University of Oxford

    • Marti Puig Fantauzzi

      University of Oxford

    • Daniel E Eakins

      University of Oxford, Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom

    • Antoine Jerusalem

      University of Oxford

    • Simon Wilkinson

      AWE

    • Mashroor S Nitol

      Los Alamos National Laboratory

    • Patrick G Heighway

      University of Oxford

    View abstract →

  • Data-Driven Dynamics: Machine Learned Interatomic Potential for Simulating Materials Under Extreme Shock Conditions

    ORAL

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    Presenters

    • Jared K Averitt

      Los Alamos National Laboratory

    Authors

    • Jared K Averitt

      Los Alamos National Laboratory

    • Chun-Shang Wong

      Los Alamos National Laboratory (LANL)

    • Eric N Loomis

      Los Alamos National Laboratory (LANL), Los Alamos National Laboratory

    • Nicholas Sirica

      Los Alamos National Laboratory (LANL)

    • David S Montgomery

      Los Alamos National Laboratory (LANL)

    • Pawel Kozlowski

      Los Alamos National Laboratory

    • Tyler Eastmond

      HPCAT, X-ray Science Division, Argonne National Laboratory, Argonne National Laboratory

    • Rohit Berlia

      Arizona State University

    • Shruti Sharma

      State Univ of NY - Stony Brook

    • Jagannathan Rajagopalan

      Arizona State University

    • Pedro Peralta

      Arizona State University

    • Pinaki Das

      Washington State University

    • Adam Schuman

      Washington State University

    • Nicholas Sinclair

      Washington State University

    • Richard Alma Messerly

      Los Alamos National Laboratory (LANL)

    • Nicholas E Lubbers

      Los Alamos National Laboratory (LANL)

    • Travis Jones

      Los Alamos National Laboratory (LANL)

    • Kipton Marcos Barros

      Los Alamos National Laboratory (LANL)

    • Sergei Tretiak

      Los Alamos National Laboratory (LANL)

    • Bejamin T Nebgen

      Los Alamos National Laboratory (LANL), Los Alamos National Laboratory

    View abstract →