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Integrated Machine Learning and Comp...

Karuthedathkuzhiyil, Anas,

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Integrated Machine Learning and Computational Framework for Predicting the Thermophysical and Functional Properties of Polymers /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Integrated Machine Learning and Computational Framework for Predicting the Thermophysical and Functional Properties of Polymers // Anas Karuthedathkuzhiyil.
Author:	Karuthedathkuzhiyil, Anas,
Description:	1 electronic resource (174 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Polymer chemistry. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30250144
ISBN:	9798379594572

Integrated Machine Learning and Computational Framework for Predicting the Thermophysical and Functional Properties of Polymers /
Karuthedathkuzhiyil, Anas,

Integrated Machine Learning and Computational Framework for Predicting the Thermophysical and Functional Properties of Polymers /Anas Karuthedathkuzhiyil. - 1 electronic resource (174 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Accelerating the pace of discovery and deployment of advanced polymeric materials is crucial for achieving global competitiveness. The fast-track development of polymeric materials requires new fundamental knowledge of structure-property-morphology relationships to predict, control, and manipulate the thermophysical and functional properties. The synergistic framework obtained by integrating data-driven machine learning (ML) and multiscale molecular modeling (MD) methods can provide molecular level insights into the thermophysical and functional properties of polymeric materials. This dissertation aims to employ data-driven ML methods in tandem with multiscale molecular modeling to gain deeper insights into glass transition temperature (Tg), hygrothermal degradation mechanism, and fouling release (FR) properties of polymeric materials. In a first study, the integrated framework by combining data-driven ML methods and coarse-grained (CG)-MD simulations are used to identify the key structural and functional attributes that govern the Tg of polymers. Informed by the ML model, CG-MD simulations are performed to further delineate mechanistic interpretation and systematic dependence of these influential molecular features on Tg by investigating three major CG model parameters, namely the cohesive interaction chain stiffness, and grafting density. In another study, the reactive molecular dynamics (ReaxFF) is applied to gain a molecular-level understanding of the hygrothermal degradation of crosslinked epoxy polymers. We believe our results provide new insights into the reversible and irreversible hygrothermal aging process in the epoxy network and inform us about the new design criteria for broadening the application of epoxy materials, particularly in humid environments. Next, an innovative modeling framework is developed by integrating machine learning (ML), statistical thermodynamics, molecular dynamics simulations, and surface characterization techniques to gain deeper insights into the fouling release (FR) mechanism of phenylmethyl silicone oil modified PDMS based coatings. We believe the synergetic framework established in this dissertation work by combining data-driven ML techniques and multiscale molecular modeling methods is important milestone in the methodology development to design and perform structure-property predictions of polymers, paving the way for accelerated discovery and deployment of polymeric materials for multifunctional applications.

English

ISBN: 9798379594572Subjects--Topical Terms:

3173488
Polymer chemistry.
Subjects--Index Terms:

Machine learning

Integrated Machine Learning and Computational Framework for Predicting the Thermophysical and Functional Properties of Polymers /
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520 $a Accelerating the pace of discovery and deployment of advanced polymeric materials is crucial for achieving global competitiveness. The fast-track development of polymeric materials requires new fundamental knowledge of structure-property-morphology relationships to predict, control, and manipulate the thermophysical and functional properties. The synergistic framework obtained by integrating data-driven machine learning (ML) and multiscale molecular modeling (MD) methods can provide molecular level insights into the thermophysical and functional properties of polymeric materials. This dissertation aims to employ data-driven ML methods in tandem with multiscale molecular modeling to gain deeper insights into glass transition temperature (Tg), hygrothermal degradation mechanism, and fouling release (FR) properties of polymeric materials. In a first study, the integrated framework by combining data-driven ML methods and coarse-grained (CG)-MD simulations are used to identify the key structural and functional attributes that govern the Tg of polymers. Informed by the ML model, CG-MD simulations are performed to further delineate mechanistic interpretation and systematic dependence of these influential molecular features on Tg by investigating three major CG model parameters, namely the cohesive interaction chain stiffness, and grafting density. In another study, the reactive molecular dynamics (ReaxFF) is applied to gain a molecular-level understanding of the hygrothermal degradation of crosslinked epoxy polymers. We believe our results provide new insights into the reversible and irreversible hygrothermal aging process in the epoxy network and inform us about the new design criteria for broadening the application of epoxy materials, particularly in humid environments. Next, an innovative modeling framework is developed by integrating machine learning (ML), statistical thermodynamics, molecular dynamics simulations, and surface characterization techniques to gain deeper insights into the fouling release (FR) mechanism of phenylmethyl silicone oil modified PDMS based coatings. We believe the synergetic framework established in this dissertation work by combining data-driven ML techniques and multiscale molecular modeling methods is important milestone in the methodology development to design and perform structure-property predictions of polymers, paving the way for accelerated discovery and deployment of polymeric materials for multifunctional applications.
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