Applied Machine Learning for Design and Discovery of Polymers

Presentations

• Data-driven Strategies to Navigate Sequence, Composition, and Architectural Complexity in Polymer Physics

  ORAL · Invited

  March 4, 2024, 9:00 AM – March 4, 2024, 9:36 AM

  Presenters

  • Michael A Webb

    Princeton University

  Authors

  • Michael A Webb

    Princeton University

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• Abstract Withdrawn

  ORAL Withdrawn

  March 4, 2024, 9:36 AM – March 4, 2024, 9:48 AM

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• Phase Diagram Predictions of Various Polymer Macromolecules in Solution Using Transfer Learning

  ORAL

  March 4, 2024, 9:48 AM – March 4, 2024, 10:00 AM

  Presenters

  • Jeffrey G Ethier

    Air Force Research Lab

  Authors

  • Jeffrey G Ethier

    Air Force Research Lab

  • Devin C Ryan

    UES, Inc. and Air Force Research Lab

  • Richard A Vaia

    Air Force Research Lab - WPAFB, Air Force Research Lab

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• Machine-learned closure for polymer liquid state theory

  ORAL

  March 4, 2024, 10:00 AM – March 4, 2024, 10:12 AM

  Presenters

  • Thomas E Gartner

    Lehigh University

  Authors

  • Thomas E Gartner

    Lehigh University

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• Interpretable Machine Learning of Phase Separated Microstructures in Polyurethane Block Copolymers

  ORAL

  March 4, 2024, 10:12 AM – March 4, 2024, 10:24 AM

  Presenters

  • Dominic M Robe

    University of Melbourne

  Authors

  • Dominic M Robe

    University of Melbourne

  • Adrian Menzel

    Platforms Division, Defence Science and Technology Group

  • Andrew Phillips

    Platforms Division, Defence Science and Technology Group

  • Peter Daivis

    RMIT University

  • Sarah Erfani

    University of Melbourne

  • Ellie Hajizadeh

    University of Melbourne

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• Machine Learning-accelerated Molecular Design of Innovative Polymers: Advanced manufacturing, extreme conditions, and sustainable energy solutions

  ORAL · Invited

  March 4, 2024, 10:24 AM – March 4, 2024, 11:00 AM

  Presenters

  • Ying Li

    University of Wisconsin-Madison

  Authors

  • Ying Li

    University of Wisconsin-Madison

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• Predicting aggregate morphology for varying composition and sequences in sequence-defined macromolecules

  ORAL

  March 4, 2024, 11:00 AM – March 4, 2024, 11:12 AM

  Presenters

  • Debjyoti Bhattacharya

    Penn State

  Authors

  • Debjyoti Bhattacharya

    Penn State

  • Wesley F Reinhart

    Pennsylvania State University, Penn State

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• Accelerating Copolymer Design via Machine Learning

  ORAL

  March 4, 2024, 11:12 AM – March 4, 2024, 11:24 AM

  Publication: 1. Himanshu and Patra T K, Developing Efficient Deep Learning Model for Predicting Copolymer Properties, Physical Chemistry Chemical Physics, 25, 25166 (2023), (2023 PCCP HOT Article)
  
  2. Ramesh P. S and Patra T K, Polymer sequence design via molecular simulation-based active learning, Soft Matter, 19, 282 (2023)
  
  3. Bale A, Gautham S, Patra T K, Sequence-Defined Pareto Frontier of a Copolymer Structure, Journal of Polymer Science 60, 2100 (2022)
  
  4. Patra T K, Loeffler T D, Sankaranarayanan S K R S, Accelerating copolymer inverse design using Monte Carlo tree search, Nanoscale 12, 23653 (2020)

  Presenters

  • Tarak K Patra

    Indian Institute of Technology Madras

  Authors

  • Tarak K Patra

    Indian Institute of Technology Madras

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• Predicting the Glass Transition of Complex Polymers via Integration of Machine Learning, Molecular Modeling and Experiments

  ORAL

  March 4, 2024, 11:24 AM – March 4, 2024, 11:36 AM

  Presenters

  • Wenjie Xia

    Iowa State University

  Authors

  • Wenjie Xia

    Iowa State University

  View abstract →