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Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization

Engineering and Technology

Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization

A. Giuntoli, N. K. Hansoge, et al.

Discover a groundbreaking coarse-graining approach for modeling epoxy resins that combines energy renormalization with innovative machine-learning techniques. This research, conducted by Andrea Giuntoli, Nitin K. Hansoge, Anton van Beek, Zhaoxu Meng, Wei Chen, and Sinan Keten, showcases impressive agreements in predicting material properties across various crosslinking degrees.

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Playback language: English
Abstract
This paper presents a coarse-graining (CG) approach for modeling epoxy resins, combining energy renormalization with Gaussian process surrogate models. The approach enables machine-learning informed calibration of degree-of-crosslinking (DC)-dependent CG force field parameters. The authors demonstrate excellent agreement between all-atom and CG predictions for density, Debye-Waller factor, Young’s modulus, and yield stress across various DCs for two epoxy resin systems. A surrogate model-enabled simplification of the functional forms of parameters is also introduced, improving efficiency for large-scale investigations of epoxy resin dynamics and mechanics.
Publisher
npj Computational Materials
Published On
Oct 14, 2021
Authors
Andrea Giuntoli, Nitin K. Hansoge, Anton van Beek, Zhaoxu Meng, Wei Chen, Sinan Keten
Tags
coarse-graining
epoxy resins
machine learning
degree-of-crosslinking
surrogate models
material properties

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