AI and Statistical/Thermal Physics

Presentations

• Self-assembly of electronic materials and the power of machine learning

  ORAL · Invited

  March 8, 2023, 3:00 PM – March 8, 2023, 3:36 PM

  Presenters

  • Paulette Clancy

    • Johns Hopkins University

  Authors

  • Paulette Clancy

    • Johns Hopkins University

  View abstract →

• How deep neural networks learn thermal phase transitions

  ORAL

  March 8, 2023, 3:36 PM – March 8, 2023, 3:48 PM

  Publication: [1] Julian Arnold and Frank Schäfer, Phys. Rev. X 12, 031044 (2022)

  Presenters

  • Frank Schäfer

    • CSAIL, Massachusetts Institute of Technology

  Authors

  • Julian Arnold

    • Department of Physics, University of Basel

  • Frank Schäfer

    • CSAIL, Massachusetts Institute of Technology

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• Machine learning phases of matter: Scalability and limitations

  ORAL

  March 8, 2023, 3:48 PM – March 8, 2023, 4:00 PM

  Presenters

  • Zhongzheng Tian

    • University of Virginia

  Authors

  • Zhongzheng Tian

    • University of Virginia

  • Sheng Zhang

    • University of Virginia

  • Gia-Wei Chern

    • University of Virginia

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• Learning Together: Training Interatomic Potentials to Multiple Datasets

  ORAL

  March 8, 2023, 4:00 PM – March 8, 2023, 4:12 PM

  Presenters

  • Alice Allen

    • Los Alamos National Lab

  Authors

  • Alice Allen

    • Los Alamos National Lab

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• Local force field of thermally displaced atoms in unstable bcc iron from machine learning

  ORAL

  March 8, 2023, 4:12 PM – March 8, 2023, 4:24 PM

  Presenters

  • Adrian De la Rocha Galán

    • University of Texas at El Paso

  Authors

  • Adrian De la Rocha Galán

    • University of Texas at El Paso

  • Valeria I Arteaga Muniz

    • University of Texas at El Paso

  • Blaise A Ayirizia

    • The University of Texas at El Paso

  • Sofia G Gomez

    • University of Texas at El Paso

  • Ramon J Ravelo

    • University of Texas at El Paso

  • Wibe A de Jong

    • LBNL
    • Lawrence Berkeley National Laboratory

  • Jorge A Munoz

    • University of Texas at El Paso

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• CHGNet: Pretrained Neural Network Potential for Fast and Accurate Charge-constrained Molecular Dynamics

  ORAL

  March 8, 2023, 4:24 PM – March 8, 2023, 4:36 PM

  Publication: Bowen Deng, "CHGNet: Pretrained Neural Network Potential for Fast and Accurate Charge-constrained Molecular Dynamics", To be submitted

  Presenters

  • Bowen Deng

    • University of California, Berkeley

  Authors

  • Bowen Deng

    • University of California, Berkeley

  • Peichen Zhong

    • University of California, Berkeley

  • Gerbrand Ceder

    • University of California, Berkeley

  View abstract →

• Neural-network-based interatomic potential: A case study on lithium

  ORAL

  March 8, 2023, 4:36 PM – March 8, 2023, 4:48 PM

  Presenters

  • Naman Katyal

    • University of Texas at Austin

  Authors

  • Naman Katyal

    • University of Texas at Austin

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• On-the-fly Machine Learning-Accelerated Geometry Optimization: Theoretical Screening of a Single Atom Alloy for CO₂ Electroreduction Reaction

  ORAL

  March 8, 2023, 4:48 PM – March 8, 2023, 5:00 PM

  Presenters

  • Jiyoung Lee

    • University of Texas at Austin

  Authors

  • Jiyoung Lee

    • University of Texas at Austin

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• Investigating the influence of local composition on properties in complex alloys using machine learned interatomic potentials

  ORAL

  March 8, 2023, 5:00 PM – March 8, 2023, 5:12 PM

  Presenters

  • Megan J McCarthy

    • Sandia National Laboratories

  Authors

  • Megan J McCarthy

    • Sandia National Laboratories

  • Jacob Startt

    • Sandia National Laboratories

  • Remi Dingreville

    • Sandia National Laboratories

  • Aidan P Thompson

    • Sandia National Laboratories

  • Mitchell A Wood

    • Sandia National Laboratories

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• Nanoparticle Heterogeneous Catalysis Dynamics Simulations with Machine Learned Force Fields

  ORAL

  March 8, 2023, 5:12 PM – March 8, 2023, 5:24 PM

  Publication: CJ Owen, Y Xie, JS Lim, L. Sun, B Kozinsky, 'Morpological evolution of PdAu nanoparticles from bayesian active learning and MD simulations.' In preparation.
  N Marcella, CJ Owen, Y Xie, AI Frenkel, B Kozinsky, RG Nuzzo, "Linking Machine-Learning Bayesian Force-Fields with XAFS to Understand Dynamic Nanomaterials under Reactive Atmospheres" In preparation.
  CJ Owen, N Marcella Y Xie, JS Lim, L Sun, AI Frenkel, B Kozinsky, RG Nuzzo, "Complex dynamics of the CO/Pt interaction from bayesian active learning simulations and EXAFS experiments" In preparation.

  Presenters

  • Cameron J Owen

    • Harvard University

  Authors

  • Cameron J Owen

    • Harvard University

  • Yu Xie

    • Harvard University

  • Jin Soo Lim

    • Harvard University

  • Boris Kozinsky

    • Harvard University

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• Melting and Phase Separation of SiC from Large-scale Machine Learning Molecular Dynamics

  ORAL

  March 8, 2023, 5:24 PM – March 8, 2023, 5:36 PM

  Presenters

  • Yu Xie

    • Harvard University

  Authors

  • Yu Xie

    • Harvard University

  • Senja J Ramakers

    • Robert Bosch GmbH

  • Boris Kozinsky

    • Harvard University

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• ML models for partition functions: from the prediction of thermodynamic properties to the exploration of transition pathways

  ORAL

  March 8, 2023, 5:36 PM – March 8, 2023, 5:48 PM

  Presenters

  • Caroline Desgranges

    • University of Massachusetts, Lowell
    • University of Massachusetts Lowell

  Authors

  • Jerome P Delhommelle

    • University of Massachusetts, Lowell

  • Caroline Desgranges

    • University of Massachusetts, Lowell
    • University of Massachusetts Lowell

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• Diffeomorphisms invariance is a proxy of performance in deep neural networks

  ORAL

  March 8, 2023, 5:48 PM – March 8, 2023, 6:00 PM

  Publication: Relative stability toward diffeomorphisms indicates performance in deep nets, L. Petrini, A. Favero, M. Geiger, M. Wyart.
  Part of Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

  Presenters

  • Leonardo Petrini

    • Ecole Polytechnique Federale de Lausanne

  Authors

  • Leonardo Petrini

    • Ecole Polytechnique Federale de Lausanne

  • Alessandro Favero

    • Ecole Polytechnique Federale de Lausanne

  • Mario Geiger

    • MIT
    • Swiss Federal Institute of Technology Lausanne (EPFL )

  • Matthieu Wyart

    • Ecole Polytechnique Federale de Lausanne

  View abstract →