Molecular Dynamics and Deep Learning for Materials Including TMDC & Oxide Moire Structures: I

FOCUS · MAR-Q49 · ID: 3104492

Presentations

  • ORAL · Invited

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    Presenters

    • Ken-ichi Nomura

      • University of Southern California

    Authors

    • Ken-ichi Nomura

      • University of Southern California

    • Aiichiro Nakano

      • University of Southern California

    • Rajiv K Kalia

      • University of Southern California

    • Tian Sang

      • University of Southern California

    • Ankit Mishra

      • University of Southern California

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  • ORAL

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    Presenters

    • Anikeya Aditya

      • University of Southern California

    Authors

    • Anikeya Aditya

      • University of Southern California

    • Ken-ichi Nomura

      • University of Southern California

    • Nitish Baradwaj

      • University of Southern California

    • Aiichiro Nakano

      • University of Southern California

    • Rajiv K Kalia

      • University of Southern California

    • Priya Vashishta

      • University of Southern California

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  • ORAL

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    Publication: J. D. Georgaras*, A. Ramdas* , and F. H. da Jornada, in preparation.

    Presenters

    • Akash Ramdas

      • Stanford University

    Authors

    • Akash Ramdas

      • Stanford University

    • Johnathan Dimitrios Georgaras

      • Stanford University

    • Felipe H da Jornada

      • Stanford University

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  • ORAL

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    Publication: Siddiqui, A., Hine, N.D.M. Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys. npj Comput Mater 10, 169 (2024). https://doi.org/10.1038/s41524-024-01357-9

    Presenters

    • Anas Siddiqui

      • University of Warwick

    Authors

    • Anas Siddiqui

      • University of Warwick

    • Nicholas D Hine

      • University of Warwick

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  • ORAL · Invited

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    Publication: 1. R. Lot, F. Pellegrini, Y. Shaidu and E. Kucukbenli, PANNA: Properties from artificial neural network architectures. Computer Physics Communications 256 (2020) 107402
    2. Y. Shaidu, R. Lot, F. Pellegrini, Kucukbenli E. and de Gironcoli S., A systematic approach to generating accurate neural network
    potentials: the case of carbon, npj Computational Materials (2021) 52 7
    3. F. Pellegrini, R. Lot, Y. Shaidu and E. Kucukbenli "PANNA 2.0: Efficient neural network interatomic potentials and new architectures." J. Chem. Phys. 159, 084117 (2023).
    4. Y. Shaidu, A. Smith, E. Taw and J. B. Neaton Carbon Capture Phenomena in Metal-Organic Frameworks with Neural Network Potentials. PRX Energy, 2023, 2.2: 023005.
    5. K. J. Kotoko, K. Sodoga, Y. Shaidu, N. Seriani, S. Borah, and K. Beltako, Uniaxial Tensile-Induced Phase Transition in Graphynes, J. Phys. Chem. C 2024, 128, 17058−17072
    6. Y. Shaidu, W. DeSnoo, A. Smith, E. Taw, and J. B. Neaton, Entropic Effects on Diamine Dynamics and CO2 Capture in Diamine-
    Appended Mg2(dopbdc) Metal−Organic Frameworks. J. Phys. Chem. Lett. 2024, 15, 1130−11
    7. Y. Shaidu, F. Pellegrini, R. Lot, Kucukbenli E. and de Gironcoli S., Incorporating long-range electrostatics in neural network potentials via variational charge equilibration from shortsighted ingredients npj Computational Materials (2024) 10 47
    8. Shaidu Y. et al. Accurate Dispersion-Aware Neural Network Potentials for Twisted Bilayer Transition Metal Dichalcogenides, in preparation, 2024.

    Presenters

    • Yusuf Shaidu

      • University of California, Berkeley

    Authors

    • Yusuf Shaidu

      • University of California, Berkeley

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  • ORAL

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    Presenters

    • Iman Ahmadabadi

      • University of Maryland College Park and Princeton University

    Authors

    • Iman Ahmadabadi

      • University of Maryland College Park and Princeton University

    • Michael Ruggenthaler

      • Max Planck Institute for the Structure and Dynamics of Matter

    • Johannes Flick

      • CCNY, CUNY GC, Simons Foundation (Flatiron Institute)
      • City College of New York
      • City College of New York and Flatiron Institute's Center for Computational Quantum Physics (CCQ)

    • Angel Rubio

      • Max Planck Institute for the Structure & Dynamics of Matter
      • Max Planck Institute for the Structure & Dynamics of Matter; Flatiron Institute's Center for Computational Quantum Physics (CCQ) & Initiative for Computational Catalysis (ICC)

    View abstract →