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Quantum-Accurate Atomistic Simulations at Extreme Scales: Recent Advances and New Challenges

INVITED · S50 · ID: 785419

Presentations

  • A Universal Interatomic Potential for the Periodic Table

    ORAL · Invited

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    Publication: (1) Chen, C.; Ong, S. P. A Universal Graph Deep Learning Interatomic Potential for the Periodic Table. arXiv:2202.02450 [cond-mat, physics:physics] 2022.

    Presenters

    • Shyue Ping Ong

      University of California, San Diego

    Authors

    • Shyue Ping Ong

      University of California, San Diego

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  • Atomistic simulation of solid-solid phase transition from machine learning force fields

    ORAL · Invited

    –

    Publication: 1. Santos-Florez P. A., Yanxon H,Kang B, Yao Y-S, Zhu Q (2022). Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics, Phys. Rev. Lett. (in press)<br>2. Santos-Florez P. A., Dai S-C, Yao Y, Yanxon H, Li L,Wang Y-J, Zhu Q, Yu X-X (2022). Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential (arXiv: 2207.09010)<br>3. Yanxon H., Zagaceta D., Tang B., Matteson D., Zhu Q. (2020). PyXtal FF: a Python Library for Automated Force Field Generation. Mach. Learn.: Sci. Technol. 2, 027001

    Presenters

    • Qiang Zhu

      University of Nevada, Las Vegas

    Authors

    • Qiang Zhu

      University of Nevada, Las Vegas

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  • Training Machine learned Interatomic Potentials to EXAFS Data for Simulations Under Extreme Conditions

    ORAL · Invited

    –

    Publication: Smith, J. S.; Nebgen, B.; Mathew, N.; Chen, J.; Lubbers, N.; Burakovsky, L.; Tretiak, S.; Nam, H. A.; Germann, T.; Fensin, S.; Barros, K., "Automated discovery of a robust interatomic potential for aluminum" Nature Comm. 2021, 12.<br>

    Presenters

    • Ben T Nebgen

      Los Alamos Natl Lab

    Authors

    • Ben T Nebgen

      Los Alamos Natl Lab

    • David S Montgomery

      Los Alamos Natl Lab

    • Eric N Loomis

      Los Alamos Natl Lab

    • Tim Wong

      Los Alamos National Laboratory

    • Sakib Matin

      Boston University, Los Alamos National Laboratory

    • Kipton M Barros

      Los Alamos Natl Lab, Theoretical Division and CNLS, Los Alamos National Laboratory

    • Richard A Messerly

      Los Alamos National Laboratory

    • Pawel Kozlowski

      Los Alamos National Laboratory

    • Pedro Peralta

      Arizona State University

    View abstract →