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Machine Learning and Data in Polymer Physics II

FOCUS · G34 · ID: 354553

Presentations

  • Machine Learning and Data in Polymer Physics Research - Interpretation of Experiments, Model Development, and Enhanced Sampling

    Invited

    –

    Presenters

    • Juan De Pablo

      University of Chicago, Pritzker School of Molecular Engineering, University of Chicago, Institute for Molecular Engineering, University of Chicago. Argonne National Laboratory, Pritzker School of Molecular Engineerin, The University of Chicago, Molecular Engineering, University of Chicago

    Authors

    • Juan De Pablo

      University of Chicago, Pritzker School of Molecular Engineering, University of Chicago, Institute for Molecular Engineering, University of Chicago. Argonne National Laboratory, Pritzker School of Molecular Engineerin, The University of Chicago, Molecular Engineering, University of Chicago

    View abstract →

  • Neural Network Accelerated Self-Consistent Field Theory

    ORAL

    –

    Presenters

    • Alfredo Alexander-Katz

      Massachusetts Institute of Technology MIT, MIT, Materials Science and Engineering, Massachusetts Institute of Technology MIT, Department of Materials Science & Engineering, Massachusetts Institute of Technology, Massachusetts Institute of Technology

    Authors

    • Hejin Huang

      Materials Science and Engineering, Massachusetts Institute of Technology MIT

    • Karim Gadelrab

      Bosch USA, Research and Technology Center, Robert Bosch LLC

    • Alfredo Alexander-Katz

      Massachusetts Institute of Technology MIT, MIT, Materials Science and Engineering, Massachusetts Institute of Technology MIT, Department of Materials Science & Engineering, Massachusetts Institute of Technology, Massachusetts Institute of Technology

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  • Predicting the glass transition behaviors of polymers via integration of molecular simulations, theory, and machine learning

    ORAL

    –

    Presenters

    • Wenjie Xia

      Civil and Environmental Engineering, north dakota state university, North Dakota State Univ

    Authors

    • Wenjie Xia

      Civil and Environmental Engineering, north dakota state university, North Dakota State Univ

    • Amirhadi Alesadi

      Civil and Environmental Engineering, north dakota state university, North Dakota State Univ

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  • Extracting molecular mechanisms of shear-thinning of liquids at high strain rates using machine learning

    ORAL

    –

    Presenters

    • Vikram Jadhao

      Intelligent Systems Engineering, Indiana University Bloomington, Intelligent Systems Engineering, Indiana Univ - Bloomington, Indiana Univ - Bloomington, Intelligent Systems Engineering, Indiana University

    Authors

    • Vikram Jadhao

      Intelligent Systems Engineering, Indiana University Bloomington, Intelligent Systems Engineering, Indiana Univ - Bloomington, Indiana Univ - Bloomington, Intelligent Systems Engineering, Indiana University

    • JCS Kadupitiya

      Intelligent Systems Engineering, Indiana University Bloomington, Intelligent Systems Engineering, Indiana Univ - Bloomington

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  • Hybrid machine learning/materials science modeling for semi-crystalline polymer during film fabrication process

    ORAL

    –

    Presenters

    • Jian Yang

      The Dow Chemical Company

    Authors

    • Jian Yang

      The Dow Chemical Company

    • Teresa Karjala

      The Dow Chemical Company

    • Jonathan Mendenhall

      The Dow Chemical Company

    • Valeriy Ginzburg

      Dow Chemical, Dow Chemical Company Foundation, Dow Chemical Co, The Dow Chemical Company

    • Rajen Patel

      The Dow Chemical Company

    • Fawzi Hamad

      The Dow Chemical Company

    • Elva Lugo

      The Dow Chemical Company

    • Pavan Valavala

      The Dow Chemical Company

    View abstract →

  • Developing Databases for Polymer Informatics

    ORAL

    –

    Presenters

    • Debra Audus

      National Institute of Standards and Technology, National Institute of Standards and Technology, Gaithersburg, MD

    Authors

    • Roselyne Tchoua

      DePaul University

    • Zhi Hong

      University of Chicago

    • Debra Audus

      National Institute of Standards and Technology, National Institute of Standards and Technology, Gaithersburg, MD

    • Shrayesh Patel

      University of Chicago

    • Logan Ward

      University of Chicago

    • Kyle Chard

      University of Chicago

    • Juan De Pablo

      University of Chicago, Pritzker School of Molecular Engineering, University of Chicago, Institute for Molecular Engineering, University of Chicago. Argonne National Laboratory, Pritzker School of Molecular Engineerin, The University of Chicago, Molecular Engineering, University of Chicago

    • Ian Foster

      University of Chicago

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  • Data Science and Machine Learning for polymer films and beyond

    Invited

    –

    Presenters

    • Daniela Ushizima

      CAMERA, Lawrence Berkeley National Laboratory

    Authors

    • Daniela Ushizima

      CAMERA, Lawrence Berkeley National Laboratory

    • Marcus Noack

      CAMERA, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory

    • Alexander Hexemer

      CAMERA, Lawrence Berkeley National Laboratory

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  • Parameter Estimation for Spatio-Temporal Models using Bayesian Optimisation and Gaussian Processes

    ORAL

    –

    Presenters

    • Nigel Clarke

      Department of Physics and Astronomy, University of Sheffield

    Authors

    • Nigel Clarke

      Department of Physics and Astronomy, University of Sheffield

    • Joao Cabral

      Imperial College London, Department of Chemical Engineering, Imperial College

    • Richard Wilkinson

      School of Mathematics and Statistics, University of Sheffield

    • Wil Ward

      Department of Physics and Astronomy, University of Sheffield

    • Sebastian Pont

      Department of Chemical Engineering, Imperial College

    View abstract →