Giuntoli, Andrea published the artcileSystematic coarse-graining of epoxy resins with machine learning-informed energy renormalization, COA of Formula: C13H26N2, the publication is npj Computational Materials (2021), 7(1), 168, database is CAplus and MEDLINE.
Abstract: A persistent challenge in mol. modeling of thermoset polymers is capturing the effects of chem. composition and degree of crosslinking (DC) on dynamical and mech. properties with high computational efficiency. We established a coarse-graining (CG) approach combining the energy renormalization method with Gaussian process surrogate models of mol. 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 d., Debye-Waller factor, Young’s modulus, and yield stress at any DC. We further introduced a surrogate model-enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of calibration functions. The framework established provides an efficient methodol. for chem.-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.
npj Computational Materials published new progress about 1761-71-3. 1761-71-3 belongs to quinuclidine, auxiliary class Ploymers, name is 4,4-Diaminodicyclohexyl methane, and the molecular formula is C13H26N2, COA of Formula: C13H26N2.
Referemce:
https://en.wikipedia.org/wiki/Quinuclidine,
Quinuclidine | C7H13N | ChemSpider