1. Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
2. Advanced Materials Laboratory, Fudan University, Shanghai 200433, China
Microwave absorbing materials are mostly composite materials, which play an important role in resisting electromagnetic interference and electromagnetic radiation. Their permeability is related to the storage and consumption of magnetic energy, as well as the impedance matching of the materials. In this work, microwave absorbing material of carbonyl iron/ferroferric oxide composite is taken as the research object, the machine learning models of the integral value of the real part of permeability (∫μ′) and the integral value of the imaginary part of permeability (∫μ″) are constructed by using random forest regression (RFR) algorithm and support vector regression (SVR) algorithm respectively, with six ball milling process parameters as feature variables. Three virtual samples are designed by two-step high-throughput screening, considering both ∫μ′ and ∫μ″, and the experimental verification is carried out. The results show that the relative prediction error of ∫μ′ is 3.14%, and the relative prediction error of ∫μ″ is -6.56%. Therefore, this research method can be used to explore the relationship between process parameters and absorbing properties of microwave absorbing materials, providing new ideas for the optimal design of microwave absorbing materials by using machine learning.