Intelligent design of multifunctional graphene-based aerogels for ultra-broadband microwave absorption

Abstract

The growing demand for microwave absorbing materials to mitigate electromagnetic pollution has driven the exploration of efficient design strategies. However, traditional experimental approaches for optimizing multi-component and multilayer structures are time-consuming. To rapidly predict and optimize the electromagnetic parameters of microwave absorbing materials, a machine learning-assisted design framework has been proposed. A series of graphene/SiO₂ and graphene/BaTiO₃ aerogels were prepared using electrospinning technology, and their electromagnetic parameter datasets were used to train a machine learning model. The model achieved a maximum prediction accuracy of 97.3%, significantly accelerating the design process. By integrating the predicted parameters into simulation software, gradient impedance structures were rapidly designed, yielding multifunctional aerogels with an ultra-wideband absorption range of 3.26–17.30 GHz at a thickness of 20 mm. Compared to conventional methods, this machine learning strategy reduces the research cycle to mere weeks, enabling the fast and efficient design of high-performance absorbing materials. Additionally, the aerogel demonstrated excellent thermal insulation and soundproofing capabilities, underscoring its multifunctionality. This study demonstrates the potential of machine learning in accelerating the development of next-generation microwave absorbing materials.