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

FOCUS · MAR-Q49 · ID: 3104492

Presentations

  • Deep Reinforcement Learning for Slow Diffusion Processes in Materials

    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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  • Neural Network Dynamics for Barium Titanate (BaTiO3) Moire Structures

    ORAL

    –

    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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  • Composite verifiable ML potentials for moiré materials

    ORAL

    –

    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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  • Raman Spectroscopy of Transition Metal Dichalcogenide Mo<sub>1-x</sub>W<sub>x</sub>S<sub>2-2y</sub>Se<sub>2y</sub> Alloys through Machine-Learned Force Fields

    ORAL

    –

    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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  • Advancing Neural Network Potentials for the Temperature-Dependent Dynamics of Complex Energy Materials

    ORAL · Invited

    –

    Publication: 1. R. Lot, F. Pellegrini, Y. Shaidu and E. Kucukbenli, PANNA: Properties from artificial neural network architectures. Computer Physics Communications 256 (2020) 107402<br>2. Y. Shaidu, R. Lot, F. Pellegrini, Kucukbenli E. and de Gironcoli S., A systematic approach to generating accurate neural network<br>potentials: the case of carbon, npj Computational Materials (2021) 52 7<br>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).<br>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.<br>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<br>6. Y. Shaidu, W. DeSnoo, A. Smith, E. Taw, and J. B. Neaton, Entropic Effects on Diamine Dynamics and CO2 Capture in Diamine-<br>Appended Mg2(dopbdc) Metal−Organic Frameworks. J. Phys. Chem. Lett. 2024, 15, 1130−11<br>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<br>8. Shaidu Y. et al. Accurate Dispersion-Aware Neural Network Potentials for Twisted Bilayer Transition Metal Dichalcogenides, in preparation, 2024.<br>

    Presenters

    • Yusuf Shaidu

      University of California, Berkeley

    Authors

    • Yusuf Shaidu

      University of California, Berkeley

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  • Influence of vibrational strong coupling on intermolecular interactions in liquid water via cavity molecular dynamics simulation

    ORAL

    –

    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)

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