Abstract
A persistent challenge in molecular modeling of thermoset polymers is capturing the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a coarse-graining (CG) approach combining the energy renormalization method with Gaussian process surrogate models of 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 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 methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.
Highlights
Computational design of high-performance epoxy resins calls for methods to circumvent costly experiments
Any fixed parametrization of the CG model is not able to match the properties of the AA system at all degree of crosslinking (DC) values, as we show in Supplementary Figs. 2 and 3 in our Supplementary Note 2
The development of new epoxy resin composites for nextgeneration materials requires an understanding of how the macroscopic properties of the system emerge from its molecular structure, with a level of precision hard to achieve in experiments, and at scales unachievable with AA molecular dynamics (MD) simulations
Summary
Computational design of high-performance epoxy resins calls for methods to circumvent costly experiments. Chemistry-specific molecular models are critically needed to bridge the gap in scales between molecular dynamics (MD) simulations and experiments, while predicting accurately the highly tunable macroscopic properties of epoxy resins and their composites[1,2,3] This remains a challenging problem to tackle due to the chemical complexity[4,5,6] of epoxy resins, the high number of properties that must be targeted for realistic predictions, and their strong dependence on the degree of crosslinking (DC)[7,8,9,10,11,12]. Allatom (AA) MD simulations have demonstrated great success in predicting the effect of DC on the glass-transition temperature (Tg), thermal expansion coefficient and elastic response[13,14] of epoxy resins, and the fracture behavior of epoxy composites[15,16] This makes AA-MD suitable for informing larger-scale models, provided that the data required for upscaling is not prohibitively expensive to obtain. While theoretical tools such as timetemperature superposition have been instrumental in bridging temporal scales[17,18], AA simulations on their own remain prohibitively expensive for high-throughput design
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