基于机器学习-能量重整化的环氧树脂系统粗晶化
Systematic Coarse-graining of Epoxy Resins with Machine Learning-Informed Energy Renormalization
摘要：
热固性聚合物的预测性分子建模中的一个持续挑战是以高计算效率捕获化学组成和交联度（DC）对动态和机械性能的影响。将能量重整化方法与高斯过程代理模型相结合，建立了一种新的分子动力学模拟粗粒化方法。这允许机器学习告知DC相关CG力场参数的功能校准。以多功能环氧树脂组成的双酚A二缩水甘油醚与固化剂的4，4-二氨基二环己基甲烷或聚氧丙烯二胺，我们证明了良好的协议之间的所有原子和CG预测密度，德拜-沃勒因子，杨氏模量和屈服应力在任何DC。我们进一步介绍了一个代理模型，使简化的功能形式的14个非保税校准参数量化的候选集的高维和灵活的校准功能的不确定性。建立的框架提供了一个有效的方法化学特定的，大规模的环氧树脂的动力学和力学的调查。
英文摘要：
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.
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