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.