Nanoreinforced polymer composites have been widely investigated and developed due to their excellent physical and chemical properties, and they have been broadly applied in aerospace engineering, microelectronic packaging, and electronic and semiconductor devices. In recent years, numerous studies have focused on quantifying the influence of nanoscale fillers on the performance of these composites. However, deterministic models often overlook material uncertainties, and stochastic multiscale modeling is computationally expensive. With the advancement of high-performance computing, machine learning—well-known for its efficient modeling capabilities—has become increasingly popular for addressing these uncertainties.
Based on this motivation, we developed a multiscale and multi-tier framework that integrates theoretical modeling with experimental applications. Functional nanocomposites are designed from the micro- to macro-scale according to engineering requirements, followed by laboratory testing of their properties. The tested materials are then deployed in real environments for field measurements and application validation. The results demonstrate that this multiscale and multi-tier framework is capable of designing optimal materials tailored to engineering needs while enabling a seamless transition from theoretical simulation to experimental implementation for polymer nanocomposites.
Our study highlights an effective pathway for the design and development of nanoreinforced polymer composites through a multiscale and multi-tier strategy, allowing them to better meet practical engineering demands. This approach provides new ideas and tools for the future design and development of intelligent advanced materials and is expected to further promote the application and evolution of functional nanocomposites across various fields.