Predictive computational modeling of polymer materials is necessary for the efficient design of composite materials and the corresponding processing methods. Molecular dynamics (MD) modeling is especially important for establishing accurate processing-structure-property relationships for neat resins. For MD modeling of amorphous polymer materials, an accurate force field is fundamental to reliable prediction of properties. Reactive force fields, in which chemical bonds can be formed or broken, offer further capability in predicting the mechanical behavior of amorphous polymers subjected to relatively large deformations. To this end, the Reactive Interface Force Field (IFF-R) has been recently developed to provide an efficient means to predict the behavior of materials under these conditions. Although IFF-R has been proven to be accurate for some crystalline organic and inorganic systems, it has not yet been proven to be accurate for amorphous polymer systems. The objective of this study is to use IFF-R to predict the thermo-mechanical properties of three different epoxy systems and validate with experimental measurements. The results indicate that IFF-R predicts thermo-mechanical properties that agree closely with experiment. Therefore, IFF-R can be used to reliably establish mechanical properties of polymers on the molecular level for future design of new composite materials and processing methods.
Thermo-elastic properties of hydrated epoxy-graphene nanocomposites from ensemble-based molecular dynamics simulations
Univ. Rennes, CNRS, IPR (Institut de Physique de Rennes) - UMR 6251（雷恩大学）；Centre for Computational Science - University College London（伦敦大学学院计算科学中心）；Hexcel Composites（海克斯尔复合材料公司）
Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
Sindu B.S Special and Multifunctional Structures Laboratory, CSIR-Structural Engineering Research Centre, Taramani, Chennai, Tamil Nadu, India（Sindu B.S 特殊和多功能结构实验室，CSIR-结构工程研究中心，坦马尼，钦奈，泰米尔纳德邦，印度）；Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Schloss Birlinghoven, 53757 Sankt Augustin , Germany（弗劳恩霍夫算法与科学计算研究所SCAI，伯灵霍芬城堡，53757圣奥古斯滕，德国）
Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design
