Machine Learning of Molecules and Materials: Electronic Structure I

Presentations

• Machine learning the Hohenberg-Kohn map to electronic excited states

  ORAL

  March 6, 2024, 9:00 AM – March 6, 2024, 9:12 AM

  Publication: Y. Bai, L. Vogt-Maranto, M. E. Tuckerman, W. J. Glover, "Machine learning the Hohenberg-Kohn map to molecular excited states", Nat. Commun. 13, 7044 (2022)

  Presenters

  • William J Glover

    NYU Shanghai

  Authors

  • Yuanming Bai

    NYU Shanghai

  • Leslie Vogt-Maranto

    New York University (NYU)

  • Mark E Tuckerman

    New York University (NYU), New York Univ NYU

  • William J Glover

    NYU Shanghai

  View abstract →

• Equivariant neural network for lattice models in condensed matter systems

  ORAL

  March 6, 2024, 9:12 AM – March 6, 2024, 9:24 AM

  Presenters

  • Yunhao Fan

    University of Virginia

  Authors

  • Gia-Wei Chern

    University of Virginia

  • Yunhao Fan

    University of Virginia

  View abstract →

• Improving the Accuracy of Machine-Learning-Based Exchange Correlation Functionals for Predicting Electronic Properties

  ORAL

  March 6, 2024, 9:24 AM – March 6, 2024, 9:36 AM

  Presenters

  • Xinyuan Liang

    Peking Univ

  Authors

  • Xinyuan Liang

    Peking Univ

  • Mohan Chen

    HEDPS, CAPT, College of Engineering and School of Physics, Peking University, Beijing 100871, Peking University, Peking Univ, Peking Unversity

  View abstract →

• Computational Graph Representation of Multiloop Feynman Diagrams

  ORAL

  March 6, 2024, 9:36 AM – March 6, 2024, 9:48 AM

  Presenters

  • Tao Wang

    University of Massachusetts Amherst

  Authors

  • Tao Wang

    University of Massachusetts Amherst

  • Pengcheng Hou

    University of Science and Technology of China

  • Daniel P Cerkoney

    Rutgers University, New Brunswick, Rutgers University

  • Zhiyi Li

    University of science and technology of China

  • Xiansheng Cai

    University of Massachusetts Amherst

  • Kun Chen

    Flatiron Institute, Center for Computational Quantum Physics

  View abstract →

• Machine Learning Many-Body Green's Functions for Molecular Excitation Spectra

  ORAL

  March 6, 2024, 9:48 AM – March 6, 2024, 10:00 AM

  Presenters

  • Tianyu Zhu

    Yale University

  Authors

  • Tianyu Zhu

    Yale University

  • Christian Venturella

    Yale University

  • Christopher Hillenbrand

    Yale University

  • Jiachen Li

    Yale University

  View abstract →

• Machine-learning tight-binding model for large-scale electronic-structure calculations

  ORAL

  March 6, 2024, 10:00 AM – March 6, 2024, 10:12 AM

  Publication: M. Schwade, M.J. Schilcher C. Reverón Baecker, M. Grumet, D. A. Egger, arXiv:2308.08897 [cond-mat.mtrl-sci] (2023)

  Presenters

  • Martin Schwade

    Physics Department, TUM School of Natural Sciences, Technical University of Munich

  Authors

  • Martin Schwade

    Physics Department, TUM School of Natural Sciences, Technical University of Munich

  • David A Egger

    Physics Department, TUM School of Natural Sciences, Technical University of Munich, Department of Physics, Technical University of Munich

  View abstract →

• Machine learning the electronic structure of molecules via the one-body reduced density matrix

  ORAL

  March 6, 2024, 10:12 AM – March 6, 2024, 10:24 AM

  Presenters

  • Xuecheng Shao

    Rutgers University - Newark

  Authors

  • Xuecheng Shao

    Rutgers University - Newark

  • Mark E Tuckerman

    New York University (NYU), New York Univ NYU

  • Michele Pavanello

    Rutgers University - Newark

  View abstract →

• Transport mechanism in Lithium thiophosphate

  ORAL

  March 6, 2024, 10:24 AM – March 6, 2024, 10:36 AM

  Publication: Gigli, L.; Tisi, D.; Grasselli, F.; Ceriotti, M. Mechanism of charge transport in lithium
  thiophosphate, in preparation.
  
  Tisi, D.; Gigli, L.; Grasselli, F.; Ceriotti, M. Thermal transport in lithium thiophosphate by machine learning, in preparation.

  Presenters

  • Davide Tisi

    Ecole Polytechnique Federale de Lausanne (EPFL), Federal Institute of Technology (EPFL)

  Authors

  • Davide Tisi

    Ecole Polytechnique Federale de Lausanne (EPFL), Federal Institute of Technology (EPFL)

  • Lorenzo Gigli

    Ecole Polytechnique Federale de Lausanne (EPFL)

  • Federico Grasselli

    Ecole Polytechnique Federale de Lausanne (EPFL)

  • Michele Ceriotti

    Ecole Polytechnique Federale de Lausanne

  View abstract →

• Using Machine Learning to Establish the Importance of High-Level Electronic Structure: Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore

  ORAL · Invited

  March 6, 2024, 10:36 AM – March 6, 2024, 11:12 AM

  Publication: J. Phys. Chem. Lett. 2023, 14, 29, 6610–6619

  Presenters

  • Christine Isborn

    University of California Merced

  Authors

  • Christine Isborn

    University of California Merced

  • Thomas E Markland

    Stanford University

  • Michael Chen

    New York University, Stanford University

  • Yuezhi Mao

    San Diego State University

  • Andrew Snider

    University of California Merced

  • Prachi Gupta

    University of California Merced

  • Andres Montoya-Castillo

    University of Colorado, University of Colorado, Boulder

  • Tim J Zuehlsdorff

    Oregon State University

  View abstract →

• Reduced-density-matrix functional theory, many-body quantum resources, and machine learning

  ORAL

  March 6, 2024, 11:12 AM – March 6, 2024, 11:24 AM

  Publication: C. L. Benavides-Riveros et al., Phys. Rev. Lett. 124, 180603 (2020).
  J. Schmidt, M. Fadel, and C. L. Benavides-Riveros, Phys. Rev. Res. 3, L032063 (2021).
  C. L. Benavides-Riveros et al., Phys. Rev. Lett. 129, 066401 (2022).
  C. L. Benavides-Riveros, arXiv:2304.09056 (2023).
  C. L. Benavides-Riveros, T. Wasak, and A. Recati, to be submitted.

  Presenters

  • Carlos L Benavides-Riveros

    University of Trento

  Authors

  • Carlos L Benavides-Riveros

    University of Trento

  • Tomasz Wasak

    Nicolaus Copernicus University in Torun

  • Alessio Recati

    Pitaevskii BEC Center

  • Luis Colmenarez

    RWTH Aachen University

  View abstract →

• Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function

  ORAL

  March 6, 2024, 11:24 AM – March 6, 2024, 11:36 AM

  Presenters

  • Jiawei Zang

    Columbia University

  Authors

  • Jiawei Zang

    Columbia University

  • Andrew Millis

    Columbia University

  • Matija Medvidović

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

  • Dominik Kiese

    Center for Computational Quantum Physics, Flatiron Institute, Flatiron Institute, Simons Foundation

  • Domenico Di Sante

    University of Bologna

  • Anirvan M Sengupta

    Rutgers University, New Brunswick

  View abstract →

• Learning the density matrix, a symmetry rich encoding of the electronic density.

  ORAL

  March 6, 2024, 11:36 AM – March 6, 2024, 11:48 AM

  Publication: Planned paper to describe the method in detail and show benchmark results.
  
  Previous published approach:
  Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids. Peter Jorgensen and Arghya Bhowmik. Nature npj computational materials (2022)

  Presenters

  • Pol Febrer

    Catalan Institute of Nanoscience and Nanotechnology - ICN2

  Authors

  • Pol Febrer

    Catalan Institute of Nanoscience and Nanotechnology - ICN2

  • Arghya Bhowmik

    Denmark Technical University (DTU)

  • Miguel A Pruneda

    Institut Català de Nanotecnologia (ICN2)

  • Alberto Garcia

    Consejo Superior de Investigaciones Cientificas (CSIC)

  • Peter B Jorgensen

    Denmark Technical University (DTU)

  View abstract →

• Circumventing the many-body problem by learning the two-body reduced density matrix

  ORAL

  March 6, 2024, 11:48 AM – March 6, 2024, 12:00 PM

  Presenters

  • Jessica A. A Martinez B.

    Rutgers University - Newark

  Authors

  • Jessica A. A Martinez B.

    Rutgers University - Newark

  • Xuecheng Shao

    Rutgers University - Newark

  • Michele Pavanello

    Rutgers University - Newark

  View abstract →