Machine Learning-Guided Additive Manufacturing of Multilayer Aerogels for Ultrabroadband, Ultralow-Reflection Electromagnetic Shielding.

Abstract

The development of broadband, low-reflection electromagnetic interference (EMI) shielding materials is critically needed to suppress secondary electromagnetic pollution. Here, we report a machine learning (ML)-guided additive manufacturing strategy for precisely fabricating multilayer gradient transition metal carbides/nitrides (MXene)-based aerogels with spatially programmed electrical conductivity. Our approach synergistically integrates sustainable cellulose nanofibers with a utilization MXene dispersion, genetic algorithm-enabled structural optimization, and direct-ink writing for precise fabrication. The resulting aerogels achieve benchmark EMI shielding performance, characterized by an ultralow average reflectivity (R) of 0.045 and sustained absorptivity (A) above 0.9 over an ultrabroad bandwidth of 30.3 GHz (9.7-40.0 GHz), remarkably surpassing existing materials. This success, validated by the close agreement among ML predictions, simulations, and experiments, demonstrates a powerful data-driven paradigm. Consequently, this study establishes a comprehensive blueprint for the ML-accelerated development of next-generation, intelligent electromagnetic protection systems centered on lightweight, absorption-dominant aerogels.