Machine Learning, Autonomous Experiments, and Big Data in Polymer Physics II
FOCUS · T03 · ID: 1067176
Presentations
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Olexandr Isayev
ORAL · Invited
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Presenters
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Olexandr Isayev
Carnegie Mellon University
Authors
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Olexandr Isayev
Carnegie Mellon University
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Linking Rheological Properties with Molecular-Scale Features via Molecular Dynamics Simulations and Machine Learning
ORAL
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Presenters
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Wenhui Li
Indiana University
Authors
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Wenhui Li
Indiana University
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JCS Kadupitiya
Indiana University
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Vikram Jadhao
Indiana University Bloomington
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Active Learning Exploration of Thermally Conductive Strained Polymers
ORAL
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Presenters
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Renzheng Zhang
University of Notre Dame
Authors
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Renzheng Zhang
University of Notre Dame
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Jiaxin Xu
University of Notre Dame
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Hanfeng Zhang
University of Notre Dame
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Tengfei Luo
University of Notre Dame, Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, United States
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Connecting local structure and transport of small molecules through glassy polymers
ORAL
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Presenters
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Samuel J Layding
University of Pennsylvania
Authors
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Samuel J Layding
University of Pennsylvania
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Robert A Riggleman
University of Pennsylvania
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Deep Learning Approaches for Property Prediction and Inverse Design of Polymers
ORAL
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Presenters
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JIHUN AHN
Chonnam national university
Authors
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JIHUN AHN
Chonnam national university
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Su-Mi Hur
Chonnam National University, Chonnam Natl Univ
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Yeojin Choe
Chonnam National University
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Gabriella Pasya Irianti
Chonnam National University
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Developing Universal Machine Learning Model for Predicting Polymer Properties
ORAL
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Publication: Himanshu and Patra T K, When does deep learning fail and how to tackle it? A critical analysis on polymer?sequence-property surrogate models, https://doi.org/10.48550/arXiv.2210.06622 (2022)
Presenters
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Himanshu .
Indian Institute of Technology, Madras
Authors
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Himanshu .
Indian Institute of Technology, Madras
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Tarak K Patra
Indian Institute of Technology Madras
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Quantifying Pairwise Chemical Similarity of Polymers
ORAL
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Presenters
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Jiale Shi
Massachusetts Institute of Technology
Authors
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Jiale Shi
Massachusetts Institute of Technology
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Nathan J Rebello
Massachusetts Institute of Technology
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Dylan Walsh
Massachusetts Institute of Technology
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Weizhong Zou
Massachusetts Institute of Technology
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Michael E Deagen
Massachusetts Institute of Technology
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Debra J Audus
NIST
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Bradley D Olsen
Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology
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Canonicalizing BigSMILES for Polymer Informatics Using Chemical Intuition and State Machines
ORAL
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Publication: https://doi.org/10.1021/acspolymersau.2c00009
Presenters
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Nathan J Rebello
Massachusetts Institute of Technology
Authors
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Nathan J Rebello
Massachusetts Institute of Technology
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Tzyy-Shyang Lin
Massachusetts Institute of Technology MIT
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Guang-He Lee
Massachusetts Institute of Technology MIT
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Melody A Morris
Massachusetts Institute of Technology MIT
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Bradley D Olsen
Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology
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CG-BigSMILES: a line notation for coarse-grained polymers
ORAL
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Presenters
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Bruno S Leao
Massachusetts Institute of Technology and University of Campinas
Authors
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Bruno S Leao
Massachusetts Institute of Technology and University of Campinas
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Weizhong Zou
Massachusetts Institute of Technology
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Bradley D Olsen
Massachusetts Institute of Technology MIT, Massachusetts Institute of Technology
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Machine learning of phase diagram based on GPR for the DPD simulation of drug delivery to endothelial cells
ORAL
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Presenters
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Joao M Maia
Case Western Reserve University
Authors
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Saeed Akbari
Case Western Reserve University
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Soumya Ray
Case western reserve university
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Joao M Maia
Case Western Reserve University
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Fei Zhou
Lawrence Livermore National lab
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Xiao Chen
Lawrence Livermore National lab
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Deep Learning Boosted Langevin Field-Theoretic Simulation of Polymers
ORAL
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Publication: Daeseong Yong and Jaeup U. Kim, Macromolecules, 2022, 55, 6505-6515.
Presenters
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Jaeup Kim
Ulsan Natl Inst of Sci & Tech, UNIST
Authors
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Jaeup Kim
Ulsan Natl Inst of Sci & Tech, UNIST
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Daeseong Yong
Korea Institute for Advanced Study
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Machine learning strategies for the structure-property relationship of copolymers
ORAL
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Publication: Tao, Lei, John Byrnes, Vikas Varshney, and Ying Li. "Machine Learning Strategies for the Structure-Property Relationship of Copolymers." iScience, 25, 104585 (2022).
Presenters
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Ying Li
University of Wisconsin-Madison
Authors
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Ying Li
University of Wisconsin-Madison
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Lei Tao
University of Connecticut
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Vikas Varshney
Air Force Research Laboratory
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John Byrnes
SRI International
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Data-driven identification and analysis of the glass transition in polymer melts
ORAL
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Publication: A. Banerjee, H.-P. Hsu, K. Kremer, O. Kukharenko (manuscript to be submitted)
Presenters
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Atreyee Banerjee
Max Planck Inst
Authors
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Atreyee Banerjee
Max Planck Inst
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Hsiao-Ping Hsu
Max Planck Institute for Polymer Research
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Kurt Kremer
Max Planck Institute for Polymer Research
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Oleksandra Kukharenko
Max Planck Institute for Polymer Research
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