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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)<br>(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

    –

    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, Los Alamos National Laboratory (LANL)

    • 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)

    • Ben T Nebgen

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

    View abstract →