Machine Learning of Molecules and Materials: Electronic Structure II

FOCUS · Q60 · ID: 2159596

Presentations

  • Machine-learning for electronic structure

    ORAL · Invited

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    Publication: C. Ben Mahmoud, F. Grasselli, and M. Ceriotti, "Predicting hot-electron free energies from ground-state data," Phys. Rev. B 106(12), L121116 (2022).
    A. Grisafi, A. M. Lewis, M. Rossi, and M. Ceriotti, "Electronic-Structure Properties from Atom-Centered Predictions of the Electron Density," J. Chem. Theory Comput. 19(14), 4451–4460 (2023).
    E. Cignoni, D. Suman, J. Nigam, L. Cupellini, B. Mennucci and M. Ceriotti, "Electronic excited states from physically-constrained machine learning", arXiv:2311.00844

    Presenters

    • Michele Ceriotti

      Ecole Polytechnique Federale de Lausanne

    Authors

    • Michele Ceriotti

      Ecole Polytechnique Federale de Lausanne

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  • Electronic Structures of Mesoscopic Systems: Unlocking Opportunities with Machine Learning and Orbital-Free Embedding

    ORAL

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    Publication: Shao, X., Paetow, L., Tuckerman, M.E., Pavanello, M., Machine learning electronic structure methods based on the one-electron reduced density matrix. Nat Commun 14, 6281 (2023)

    Jessica A. Martinez B, Lukas Paetow, Johannes Tölle, Xuecheng Shao, Pablo Ramos, Johannes Neugebauer, and Michele Pavanello. Which Physical Phenomena Determine the Ionization Potential of Liquid Water? The Journal of Physical Chemistry B, 127 (24), 5470-5480 (2023)

    Xuecheng Shao, Andres Cifuentes Lopez, Md Rajib Khan Musa, Mohammad Reza Nouri, and Michele Pavanello
    Adaptive subsystem density functional theory. Journal of Chemical Theory and Computation, 18 (11), 6646-6655 (2022)

    K Jiang, X Shao, M Pavanello. Nonlocal and nonadiabatic Pauli potential for time-dependent orbital-free density functional theory. Physical Review B 104, 235110 (2021)

    Presenters

    • Michele Pavanello

      Rutgers University - Newark

    Authors

    • Michele Pavanello

      Rutgers University - Newark

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  • Spectral operator representations

    ORAL

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    Presenters

    • Austin Zadoks

      École Polytechnique Fédérale de Lausanne

    Authors

    • Austin Zadoks

      École Polytechnique Fédérale de Lausanne

    • Nicola Marzari

      Ecole Polytechnique Federale de Lausanne, THEOS, EPFL; NCCR MARVEL; LSM Paul Scherrer Insitut, EPFL, THEOS, EPFL; NCCR, MARVEL; LMS, Paul Scherrer Institut

    • Antimo Marrazzo

      University of Trieste

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  • Nonlocal neural-network distillation of many-electron density functional theory

    ORAL

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    Presenters

    • Matija Medvidović

      Columbia University; Center for Computational Quantum Physics, Flatiron Institute, Columbia University

    Authors

    • Matija Medvidović

      Columbia University; Center for Computational Quantum Physics, Flatiron Institute, Columbia University

    • Iman Ahmadabadi

      University of Maryland, College Park-Princeton University, University of Maryland, College Park - Flatiron Institute

    • Jaylyn C Umana

      The Graduate Center, City University of New York; Center for Computational Quantum Physics, Flatiron Institute, The Graduate Center, City University of New York

    • Domenico Di Sante

      University of Bologna

    • Johannes Flick

      City College of New York; The Graduate Center, City University of New York; Center for Computational Quantum Physics, Flatiron Institute, City College of New York, Center for Computational Quantum Physics, Flatiron Institute, City College of New York - Flatiron Institute

    • Angel Rubio

      Max Planck Institute for the Structure & Dynamics of Matter, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Institute for the Structure &, Max Planck Institute for the Structure & Dynamics of Matter; Center for Computational Quantum Physics, Flatiron Institute, Center for Computational Quantum Physics, Flatiron Institute, Max Planck Institute for the Structure and Dynamics of Matter - Flatiron Institute, Max Planck Institute for Structure and Dynamics of Matter

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  • Automatic differentiation approach for obtaining exchange-correlation functional derivatives

    ORAL

    –

    Presenters

    • Jaylyn C Umana

      The Graduate Center, City University of New York; Center for Computational Quantum Physics, Flatiron Institute, The Graduate Center, City University of New York

    Authors

    • Jaylyn C Umana

      The Graduate Center, City University of New York; Center for Computational Quantum Physics, Flatiron Institute, The Graduate Center, City University of New York

    • Matija Medvidović

      Columbia University; Center for Computational Quantum Physics, Flatiron Institute, Columbia University

    • Angel Rubio

      Max Planck Institute for the Structure & Dynamics of Matter, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Institute for the Structure &, Max Planck Institute for the Structure & Dynamics of Matter; Center for Computational Quantum Physics, Flatiron Institute, Center for Computational Quantum Physics, Flatiron Institute, Max Planck Institute for the Structure and Dynamics of Matter - Flatiron Institute, Max Planck Institute for Structure and Dynamics of Matter

    • Johannes Flick

      City College of New York; The Graduate Center, City University of New York; Center for Computational Quantum Physics, Flatiron Institute, City College of New York, Center for Computational Quantum Physics, Flatiron Institute, City College of New York - Flatiron Institute

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  • Accelerating electronic structure calculations using an E(3)-equivariant neural network

    ORAL

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    Publication: [1] X. Gong, H. Li, N. Zou, R. Xu, W. Duan and Y. Xu, Nat. Commun. 14, 2848 (2023).
    [2] H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan and Y. Xu, Nat. Comput. Sci. 2, 367 (2022).
    [3] H. Li, Z. Tang, X. Gong, N. Zou, W. Duan and Y. Xu, Nat. Comput. Sci. 3, 321 (2023).
    [4] Z. Tang, H. Li, P. Lin, X. Gong, G. Jin, L. He, H. Jiang, X. Ren, W. Duan and Y. Xu, arXiv:2302.08211 (2023).

    Presenters

    • Xiaoxun Gong

      University of California, Berkeley

    Authors

    • Xiaoxun Gong

      University of California, Berkeley

    • He Li

      Tsinghua University

    • Steven G Louie

      University of California at Berkeley, University of California at Berkeley and Lawrence Berkeley National Laboratory, University of California at Berkeley, and Lawrence Berkeley National Laboratory, UC-Berkeley

    • Wenhui Duan

      Tsinghua University

    • Yong Xu

      Tsinghua University

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