Machine Learning of Molecules and Materials: Chemical Space and Dynamics

Presentations

• Quantum Machine Learning

  ORAL · Invited

  March 4, 2024, 4:00 PM – March 4, 2024, 4:36 PM

  Presenters

  • O. Von Lilienfeld

    University of Toronto, Vector Institute, Technical University of Berlin

  Authors

  • O. Von Lilienfeld

    University of Toronto, Vector Institute, Technical University of Berlin

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• Machine learning and many-body molecular interactions

  ORAL

  March 4, 2024, 4:36 PM – March 4, 2024, 4:48 PM

  Publication: A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying physics?, Y. Zhai, A. Caruso, S.L. Bore, Z. Luo, F. Paesani, J. Chem. Phys. 158, 084111 (2023)

  Presenters

  • Francesco Paesani

    University of California, San Diego

  Authors

  • Francesco Paesani

    University of California, San Diego

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• Thermodynamic and electronic properties of water and ice: joining machine learning and manybody perturbation theory

  ORAL

  March 4, 2024, 4:48 PM – March 4, 2024, 5:00 PM

  Presenters

  • Davide Donadio

    University of California Davis

  Authors

  • Davide Donadio

    University of California Davis

  • Margaret Berrens

    University of California Davis

  • Arpan Kundu

    University of Chicago

  • Zekun Chen

    University of California Davis

  • Marcos Calegari Andrade

    Lawrence Livermore National Lab

  • Tuan Anh Pham

    Lawrence Livermore Natl Lab

  • Giulia Galli

    University of Chicago

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• Accurate thermodynamic tables for solids using Machine Learning Interaction Potentials and Covariance of Atomic Positions

  ORAL

  March 4, 2024, 5:00 PM – March 4, 2024, 5:12 PM

  Presenters

  • Mgcini K Phuthi

    University of Michigan

  Authors

  • Mgcini K Phuthi

    University of Michigan

  • Yang Huang

    Carnegie Mellon Univ

  • Michael Widom

    Carnegie Mellon University

  • Ekin D Cubuk

    Google LLC

  • Venkat Viswanathan

    University of Michigan, Carnegie Mellon University

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• AI-enhanced chemical physics simulations

  ORAL · Invited

  March 4, 2024, 5:12 PM – March 4, 2024, 5:48 PM

  Publication: [1] P. O. Dral, M. Barbatti. Nat. Rev. Chem. 2021, 5, 388.
  [2] P. Zheng, R. Zubatyuk, W. Wu, O. Isayev, P. O. Dral. Nat. Commun. 2021, 12, 7022.
  [3] A. Ullah, P. O. Dral. Nat. Commun. 2022, 13, 1930.
  [4] F. Ge, L. Zhang, Y.-F. Hou, Y. Chen, A. Ullah, P. O. Dral. J. Phys. Chem. Lett. 2023, 14, 7732.
  [5] P. O. Dral, F. Ge, Y.-F. Hou, P. Zheng, Y. Chen, M. Barbatti, O. Isayev, C. Wang, B.-X. Xue, M. Pinheiro Jr,
  Y. Su, Y. Dai, Y. Chen, S. Zhang, L. Zhang, A. Ullah, Q. Zhang, Y. Ou. J. Chem. Theory Comput. 2023,
  accepted. See MLatom.com @ XACScloud.com.

  Presenters

  • Pavlo Dral

    Xiamen University

  Authors

  • Pavlo Dral

    Xiamen University

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• Learning polarization using equivariant neural networks

  ORAL

  March 4, 2024, 5:48 PM – March 4, 2024, 6:00 PM

  Presenters

  • Stefano Falletta

    Harvard University

  Authors

  • Stefano Falletta

    Harvard University

  • Andrea Cepellotti

    Harvard University

  • Albert Musaelian

    Harvard University

  • Anders Johansson

    Harvard University

  • Chuin Wei Tan

    Harvard University

  • Boris Kozinsky

    Harvard University

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• Efficient ensemble averaging methods to study electronic structure at finite temperature from first principles calculations using neural network

  ORAL

  March 4, 2024, 6:00 PM – March 4, 2024, 6:12 PM

  Presenters

  • Niraj Aryal

    Brookhaven National Laboratory

  Authors

  • Niraj Aryal

    Brookhaven National Laboratory

  • Sheng Zhang

    University of Virginia

  • Gia-Wei Chern

    University of Virginia

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• Predicting electron dynamics in proton-irradiated small molecules by recurrent neural networks

  ORAL

  March 4, 2024, 6:12 PM – March 4, 2024, 6:24 PM

  Presenters

  • Ethan P Shapera

    Graz University of Technology

  Authors

  • Ethan P Shapera

    Graz University of Technology

  • Cheng-Wei Lee

    Colorado School of Mines

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• Efficient mapping of CO₂RR intermediates adsorption energies on Cu_1-xM_x bimetallic alloys via Machine Learning

  ORAL

  March 4, 2024, 6:24 PM – March 4, 2024, 6:36 PM

  Publication: Efficient mapping of CO adsorption on Cu1-xMx bimetallic alloys via Machine Learning (planned paper)

  Presenters

  • Mattia Salomone

    DISAT (Politecnico of Turin)

  Authors

  • Mattia Salomone

    DISAT (Politecnico of Turin)

  • Francesca Risplendi

    DISAT (Politecnico of Turin)

  • Michele Re Fiorentin

    DISAT (Politecnico of Turin)

  • Federico Raffone

    DISAT (Politecnico of Turin)

  • Alejandro Cañete Arché

    Trinity College Dublin, The University of Dublin

  • Timo Sommer

    Trinity College Dublin, The University of Dublin

  • Max García-Melchor

    Trinity College Dublin, The University of Dublin

  • Giancarlo Cicero

    DISAT (Politecnico of Turin)

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• Machine Learning with Semi-Empirical Quantum Mechanical Methods for Band Gap Prediction

  ORAL

  March 4, 2024, 6:36 PM – March 4, 2024, 6:48 PM

  Presenters

  • Omololu Akin-Ojo

    University of Ibadan

  Authors

  • Omololu Akin-Ojo

    University of Ibadan

  • Ezekiel Oyeniyi

    University of Ibadan

  • Adeolu O Ayoola

    University of Ibadan

  • Damilare Babatunde

    University of Ibadan

  View abstract →