Autonomous Science and Machine Learning for Polymer Characterization
FOCUS · MAR-F55 · ID: 3097635
Presentations
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Hold
COFFEE_KLATCH · Invited
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The Future of Autonomous Science
ORAL · Invited
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Presenters
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Kevin G Yager
Brookhaven National Laboratory (BNL)
Authors
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Kevin G Yager
Brookhaven National Laboratory (BNL)
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Accelerated Small Angle Neutron Scattering Algorithm for Polymeric Materials
ORAL
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Presenters
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Kexin Dai
Massachusetts Institute of Technology
Authors
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Kexin Dai
Massachusetts Institute of Technology
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Bradley David Olsen
Massachusetts Institute of Technology
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Concurrent, multi-modal, multi-facility scattering experiments on polymer materials with the Autonomous Formulation Lab
ORAL
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Presenters
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Tyler B Martin
National Institute of Standards and Technology (NIST)
Authors
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Tyler B Martin
National Institute of Standards and Technology (NIST)
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Duncan Sutherland
University of Colorado at Boulder, National Institute of Standards and Technology (NIST)
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Peter Beaucage
National Institute of Standards and Technology (NIST)
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Physics-Informed Machine Learning for Predicting SAXS Data of Lyotropic Liquid Crystals using Generative Models
ORAL
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Presenters
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Erin C Aldrich
Michigan Technological University
Authors
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Erin C Aldrich
Michigan Technological University
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Seyed Mostafa Tabatabaei
University of Oklahoma
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Tong Gao
Department of Physics, Michigan Technological University, Michigan Technological University
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Issei Nakamura
Michigan Technological University, Department of Physics, Michigan Technological University
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Reza Foudazi
University of Oklahoma
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Accessing Higher-Order Structural Information in Semiconducting Polymer GIWAXS Patterns by Predictive Modeling Using GIWAXSPal
ORAL
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Presenters
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Alexander Simafranca
University of California, Los Angeles
Authors
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Alexander Simafranca
University of California, Los Angeles
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Surface Tension Prediction of Polymers Using Machine Learning and Graph Neural Networks
ORAL
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Presenters
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Javad Tamnanloo
University of Akron
Authors
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Javad Tamnanloo
University of Akron
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Abdol Hadi Mokarizadeh
University of Akron, The University of Akron
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Farzad Toiserkani
University of Akron
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Hansini Abeysinghe
University of Akron
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Abraham Joy
Northeastern University
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Mesfin Tsige
University of Akron
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Rheo-SINDy: Finding constitutive equations from nonlinear rheological data by sparse identification of nonlinear dynamics
ORAL
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Publication: T. Sato, S. Miyamoto, and S. Kato, Rheo-SINDy: Finding a constitutive model from rheological data for complex fluids using sparse identification for nonlinear dynamics, J. Rheol., accepted for publication (DOI: 10.1122/8.0000872).
Presenters
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Takeshi Sato
Advanced Manufacturing Technology Institute, Kanazawa University
Authors
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Takeshi Sato
Advanced Manufacturing Technology Institute, Kanazawa University
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Souta Miyamoto
Department of Chemical Engineering, Graduate School of Engineering, Kyoto University
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Shota Kato
Graduate School of Informatics, Kyoto University
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Grand canonical molecular dynamics simulation of surface-initiated polymerization
ORAL
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Publication: Poudel, B.; Kremer, K. Surface-initiated polymerization via grand canonical molecular dynamics simulations (manuscript in preparation).
Presenters
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Bhuwan Poudel
Max Planck Institute for Polymer Research
Authors
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Bhuwan Poudel
Max Planck Institute for Polymer Research
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Kurt Kremer
Max Planck Institute for Polymer Research
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Abstract Withdrawn
ORAL Withdrawn
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