Systematic Coarse-graining of Epoxy Resins with Machine Learning-Informed Energy Renormalization

A persistent challenge in predictive molecular modeling of thermoset polymers is to capture the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a new coarse-graining (CG) approach that combines...

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Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Giuntoli, Andrea, Hansoge, Nitin K, Anton van Beek, Meng, Zhaoxu, Chen, Wei, Keten, Sinan
Format: Article
Language:English
Subjects:
Bisphenol A
Calibration
Chemical composition
Composition effects
Crosslinking
Curing agents
Debye-Waller factor
Diamines
Epoxy resins
Gaussian process
Granulation
Machine learning
Mathematical models
Mechanical properties
Modulus of elasticity
Molecular dynamics
Parameters
Polymers
Polypropylene glycol
Thermosetting resins
Yield stress
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Summary:A persistent challenge in predictive molecular modeling of thermoset polymers is to capture the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a new coarse-graining (CG) approach that combines the energy renormalization method with Gaussian process surrogate models of the molecular dynamics simulations. This allows a machine-learning informed functional calibration of DC-dependent CG force field parameters. Taking versatile epoxy resins consisting of Bisphenol A diglycidyl ether combined with curing agent of either 4,4-Diaminodicyclohexylmethane or polyoxypropylene diamines, we demonstrated excellent agreement between all-atom and CG predictions for density, Debye-Waller factor, Young's modulus and yield stress at any DC. We further introduce a surrogate model enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of high-dimensional and flexible calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.
ISSN:2331-8422
DOI:10.48550/arxiv.2105.05697