Machine Learning and Data in Polymer Physics II
FOCUS · G34 · ID: 354553
Presentations
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Machine Learning and Data in Polymer Physics Research - Interpretation of Experiments, Model Development, and Enhanced Sampling
Invited
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Presenters
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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
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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
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Neural Network Accelerated Self-Consistent Field Theory
ORAL
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Presenters
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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
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Hejin Huang
- Materials Science and Engineering, Massachusetts Institute of Technology MIT
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Karim Gadelrab
- Bosch USA
- Research and Technology Center, Robert Bosch LLC
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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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Neural network for phase diagrams of polymer-containing liquid mixtures
ORAL
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Presenters
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Issei Nakamura
- Michigan Technological Univ
- Physics, Michigan Technological Univ
Authors
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Issei Nakamura
- Michigan Technological Univ
- Physics, Michigan Technological Univ
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Predicting the glass transition behaviors of polymers via integration of molecular simulations, theory, and machine learning
ORAL
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Presenters
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Wenjie Xia
- Civil and Environmental Engineering, north dakota state university
- North Dakota State Univ
Authors
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Wenjie Xia
- Civil and Environmental Engineering, north dakota state university
- North Dakota State Univ
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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
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Presenters
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Vikram Jadhao
- Intelligent Systems Engineering, Indiana University Bloomington
- Intelligent Systems Engineering, Indiana Univ - Bloomington
- Indiana Univ - Bloomington
- Intelligent Systems Engineering, Indiana University
Authors
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Vikram Jadhao
- Intelligent Systems Engineering, Indiana University Bloomington
- Intelligent Systems Engineering, Indiana Univ - Bloomington
- Indiana Univ - Bloomington
- Intelligent Systems Engineering, Indiana University
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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
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Presenters
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Jian Yang
- The Dow Chemical Company
Authors
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Jian Yang
- The Dow Chemical Company
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Teresa Karjala
- The Dow Chemical Company
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Jonathan Mendenhall
- The Dow Chemical Company
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Valeriy Ginzburg
- Dow Chemical
- Dow Chemical Company Foundation
- Dow Chemical Co
- The Dow Chemical Company
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Rajen Patel
- The Dow Chemical Company
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Fawzi Hamad
- The Dow Chemical Company
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Elva Lugo
- The Dow Chemical Company
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Pavan Valavala
- The Dow Chemical Company
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Developing Databases for Polymer Informatics
ORAL
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Presenters
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Debra Audus
- National Institute of Standards and Technology
- National Institute of Standards and Technology, Gaithersburg, MD
Authors
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Roselyne Tchoua
- DePaul University
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Zhi Hong
- University of Chicago
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Debra Audus
- National Institute of Standards and Technology
- National Institute of Standards and Technology, Gaithersburg, MD
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Shrayesh Patel
- University of Chicago
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Logan Ward
- University of Chicago
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Kyle Chard
- University of Chicago
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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
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Ian Foster
- University of Chicago
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Data Science and Machine Learning for polymer films and beyond
Invited
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Presenters
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Daniela Ushizima
- CAMERA, Lawrence Berkeley National Laboratory
Authors
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Daniela Ushizima
- CAMERA, Lawrence Berkeley National Laboratory
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Marcus Noack
- CAMERA, Lawrence Berkeley National Laboratory
- Lawrence Berkeley National Laboratory
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Alexander Hexemer
- CAMERA, Lawrence Berkeley National Laboratory
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Parameter Estimation for Spatio-Temporal Models using Bayesian Optimisation and Gaussian Processes
ORAL
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Presenters
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Nigel Clarke
- Department of Physics and Astronomy, University of Sheffield
Authors
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Nigel Clarke
- Department of Physics and Astronomy, University of Sheffield
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Joao Cabral
- Imperial College London
- Department of Chemical Engineering, Imperial College
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Richard Wilkinson
- School of Mathematics and Statistics, University of Sheffield
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Wil Ward
- Department of Physics and Astronomy, University of Sheffield
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Sebastian Pont
- Department of Chemical Engineering, Imperial College
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Evolutionary couplings detect side-chain interactions in protein structures
ORAL
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Presenters
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Claus Wilke
- University of Texas at Austin
Authors
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Adam J. Hockenberry
- University of Texas at Austin
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Claus Wilke
- University of Texas at Austin
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Tracking Accelerated Aging of Cross-Linked Polyethylene Pipes by Applying Machine Learning Concepts to Infrared Spectra
ORAL
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Presenters
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Joseph D'Amico
- Univ of Guelph
Authors
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Melanie Hiles
- Univ of Guelph
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Joseph D'Amico
- Univ of Guelph
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Benjamin Morling
- Univ of Guelph
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Fatemeh Abbasi
- Univ of Guelph
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Michael Grossutti
- Univ of Guelph
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John Dutcher
- Univ of Guelph
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