Performance Optimization Engineering of Multicomponent Absorbing Materials Based on Machine Learning

Multicomponent materials are microwave-absorbing (MA) materials composed of a variety of absorbents that are capable of reaching the property inaccessible for a single component. Discovering mostly valuable properties, however, often relies on semi-experience, as conventional multicomponent MA materials’ design rules alone often fail in high-dimensional design spaces. Therefore, we propose performance optimization engineering to accelerate the design of multicomponent MA materials with desired performance in a practically infinite design space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with the expanded Maxwell–Garnett model, electromagnetic calculations, and experimental feedback; aiming at different desired performances, Ni surface@carbon fiber (NiF) materials and NiF-based multicomponent (NMC) materials with target MA performance were screened and identified out of nearly infinite possible designs. The designed NiF and NMC fulfilled the requirements for the X- and Ku-bands at thicknesses of only 2.0 and 1.78 mm, respectively. In addition, the targets regarding S, C, and all bands (2.0–18.0 GHz) were also achieved as expected. This performance optimization engineering opens up a unique and effective way to design microwave-absorbing materials for practical application.