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Machine Learning for Atomistic Simulation V: Vibrational, Transport, and Magnetic Properties

FOCUS · MAR-M50 · ID: 3104700

Presentations

  • Machine learning methods for finite-temperature full-quantum simulations: Predictive modelling of condensed phases and interfaces

    ORAL · Invited

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    Publication: [1] Kaur, H., Della Pia, F., Batatia, I., Advincula, X. R., Shi, B. X., Lan, J., Csányi, G., Michaelides, A., & Kapil, V. (2024). Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies. https://doi.org/10.48550/ARXIV.2405.20217<br><br>[2] Kapil, V., Kovács, D. P., Csányi, G., & Michaelides, A. (2023). First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. Faraday Discussions, 10.1039.D3FD00113J. https://doi.org/10.1039/D3FD00113J<br><br>[3] Musil, F., Zaporozhets, I., Noé, F., Clementi, C., & Kapil, V. (2022). Quantum dynamics using path integral coarse-graining. The Journal of Chemical Physics, 157(18), 181102. https://doi.org/10.1063/5.0120386<br><br>[4] Kapil, V., Schran, C., Zen, A., Chen, J., Pickard, C. J., & Michaelides, A. (2022). The first-principles phase diagram of monolayer nanoconfined water. Nature, 609(7927), 512–516. https://doi.org/10.1038/s41586-022-05036-x<br><br>[5] Ravindra, P., Advincula, X. R., Schran, C., Michaelides, A., & Kapil, V. (2024). Quasi-one-dimensional hydrogen bonding in nanoconfined ice. Nature Communications, 15(1), 7301. https://doi.org/10.1038/s41467-024-51124-z<br><br>[6] Ravindra, P., Advincula, X. R., Shi, B. X., Coles, S. W., Michaelides, A., & Kapil, V. (2024). Nuclear quantum effects induce superionic proton transport in nanoconfined water. arXiv. https://doi.org/10.48550/ARXIV.2410.03272

    Presenters

    • Venkat Kapil

      University College London

    Authors

    • Venkat Kapil

      University College London

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  • Thermal Conductivity Predictions with Foundation Atomistic Models

    ORAL

    –

    Publication: Póta, B., Ahlawat, P., Csányi, G., & Simoncelli, M. (2024). Thermal Conductivity Predictions with Foundation Atomistic Models. arXiv preprint arXiv:2408.00755.

    Presenters

    • Balazs Pota

      Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge

    Authors

    • Balazs Pota

      Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge

    • Paramvir Ahlawat

      Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge

    • Gabor Csanyi

      Engineering Laboratory, University of Cambridge, Applied Mechanics Group, Mechanics, Materials and Design, Department of Engineering, University of Cambridge

    • Michele Simoncelli

      Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Univ of Cambridge, TCM group, Cavendish Laboratory, University of Cambridge

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  • A universal force field for molecular and electron dynamics

    ORAL

    –

    Presenters

    • Anthony Mannino

      Stony Brook University (SUNY)

    Authors

    • Anthony Mannino

      Stony Brook University (SUNY)

    • Isidro Losada Lopez

      Univ Autonoma de Madrid

    • Simon Divilov

      Duke University

    • Eduardo Hernandez

      Consejo Superior de Investigaciones Científicas

    • Javier Junquera

      Universidad de Cantabria

    • Marivi Fernandez-Serra

      Stony Brook University

    • Jose M Soler

      Univ Autonoma de Madrid

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  • Effect of Local Water Uptake on Proton Transport in Covalent Organic Framework revealed by Machine-Learning Potentials

    ORAL

    –

    Publication: S. Minami et al., Chemistry of Materials, 2024, 36, 19, 9535-9546, DOI: 10.1021/acs.chemmater.4c01351

    Presenters

    • Saori Minami

      Toyota Central R&D Labs, Inc.

    Authors

    • Saori Minami

      Toyota Central R&D Labs, Inc.

    • Masane Kin

      DENSO CORPORATION

    • Kazuki Takahashi

      DENSO CORPORATION

    • Takashi Sato

      DENSO CORPORATION

    • Ryosuke Jinnouchi

      Toyota Central R&D Labs, Inc.

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  • Revealing the fundamental proton transport mechanism in solid acid compounds through machine learning molecular dynamics

    ORAL

    –

    Presenters

    • Menghang Wang

      Harvard University

    Authors

    • Menghang Wang

      Harvard University

    • Jingxuan Ding

      Harvard University

    • Grace Xiong

      Northwestern University

    • Ni Zhan

      Princeton University

    • Cameron John Owen

      Harvard University

    • Albert Musaelian

      Harvard University

    • Yu Xie

      Harvard University

    • Nicola Molinari

      Robert Bosch LLC

    • Ryan P Adams

      Princeton University

    • Sossina M Haile

      Northwestern University

    • Boris Kozinsky

      Harvard University

    View abstract →