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V: Machine Learning in Physics

ORAL · EE02 · ID: 1086597

Presentations

  • Variational Onsager Neural Networks (VONNs): A Thermodynamics-Based Variational Learning Strategy for Non-Equilibrium Material Modeling

    ORAL

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    Publication: Shenglin Huang, Zequn He, Bryan Chem and Celia Reina. "Variational Onsager Neural Networks (VONNs): A thermodynamics-based variational learning strategy for non-equilibrium PDEs." Journal of the Mechanics and Physics of Solids 163 (2022): 104856.

    Presenters

    • Shenglin Huang

      University of Pennsylvania

    Authors

    • Shenglin Huang

      University of Pennsylvania

    • Zequn He

      University of Pennsylvania

    • Bryan Chem

      University of Pennsylvania

    • Celia Reina

      University of Pennsylvania

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  • Dynamical models from data, including constants of motion

    ORAL

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    Publication: "Extracting Dynamical Models from Data"<br>https://arxiv.org/abs/2110.06917<br><br>"Constants of Motion from Data for Conservative and Dissipative Dynamics"<br>(document in preparation)<br>

    Presenters

    • Michael F Zimmer

      Neomath, Inc

    Authors

    • Michael F Zimmer

      Neomath, Inc

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  • Machine learning inverse problem solving for optical constants determination

    ORAL

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    Presenters

    • Mariana A Fazio

      University of Strathclyde

    Authors

    • Mariana A Fazio

      University of Strathclyde

    • Kieran Craig

      University of Strathclyde

    • Marwa Ben Yaala

      University of Strathclyde

    • Bethany McCrindle

      University of Strathclyde

    • Chalisa Gier

      University of Strathclyde

    • Callum Wiseman

      University of Strathclyde

    • Stuart Reid

      University of Strathclyde

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  • Magnetic iron-cobalt silicides discovered using machine-learning

    ORAL

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    Publication: Manuscript in preparation.

    Presenters

    • Timothy Liao

      University of Texas at Austin

    Authors

    • Timothy Liao

      University of Texas at Austin

    • Weiyi Xia

      Ames Laboratory, Iowa State University

    • Masahiro Sakurai

      Univ of Tokyo-Kashiwanoha

    • Renhai Wang

      Guangdong University of Technology

    • Chao Zhang

      Yantai University

    • Huaijun Sun

      Zhejiang A & F University, Zhejiang A&F University, Zhejiang Agriculture and Forestry University

    • Kai-Ming Ho

      Iowa State University, Ames National Laboratory

    • Cai-Zhuang Wang

      Ames Laboratory, Iowa State University, Ames National Laboratory

    • James R Chelikowsky

      University of Texas at Austin

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  • Exploring materials dataspaces by combining supervised and unsupervised machine learning

    ORAL

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    Publication: [1] M. Wilkinson et al. Sci. Data. 3, 160018 (2016)<br>[2] C. Draxl, M. Scheffler, MRS Bull. 43, 676-682 (2018)<br>[3] A. Leitherer, A. Ziletti and L. M. Ghiringhelli. Nat. Commun. 12, 6234 (2021)<br>[4] T. Meiners, T. Frolov, R.E. Rudd, et al. Nature 579, 375–378 (2020)<br>[5] Y. Yang et al. Nature 592, 60 (2021)

    Presenters

    • Andreas Leitherer

      NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin

    Authors

    • Andreas Leitherer

      NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin

    • Angelo Ziletti

      NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin

    • Christian H Liebscher

      Max-Planck-Institut für Eisenforschung

    • Timofey Frolov

      Lawrence Livermore National Laboratory

    • Luca M Ghiringhelli

      1. The NOMAD Laboratory at the FHI-MPG and IRIS-Adlershof of HU, Berlin, Germany 2. Physics Department and IRIS-Adlershof of HU, Berlin, Germany, Physics Department and IRIS-Adlershof of HU, Berlin, Germany and The NOMAD Laboratory at the FHI-MPG and HU, Berlin, Germany, NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität (HU) zu Berlin; Physics Department and IRIS-Adlershof of HU zu Berlin

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  • Development of Deep Learning Potentials to Investigate Initial Corrosion Mechanisms

    ORAL

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    Presenters

    • Ridwan Sakidja

      Missouri State University, Physics, Astronomy and Materials Science, Missouri State University

    Authors

    • Ridwan Sakidja

      Missouri State University, Physics, Astronomy and Materials Science, Missouri State University

    • Hendra Hermawan

      Laval University

    • Ayoub Tanji

      Laval University

    • Peter K Liaw

      The University of Tennessee

    • Xuesong Fan

      The University of Tennessee

    View abstract →

  • Machine learning potentials for accelerated nuclear fuel qualification

    ORAL

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    Presenters

    • Richard A Messerly

      Los Alamos National Laboratory

    Authors

    • Richard A Messerly

      Los Alamos National Laboratory

    • Leidy Lorena Alzate Vargas

      Los Alamos National Laboratory

    • Roxanne M Tutchton

      Los Alamos National Laboratory

    • Michael Cooper

      Los Alamos National Laboratory

    • Sergei Tretiak

      Los Alamos National Laboratory, Los Alamos National Lab

    • Tammie Gibson

      Los Alamos National Lab, Los Alamos National Laboratory

    View abstract →