Machine Learning-Guided Design and Performance Prediction of Multidimensional Magnetic MXene-Based Nanocomposites for High-Efficiency Microwave Absorption

MXene-based microwave absorbers have received extensive attention owing to their high electrical conductivity, abundant interfacial polarization sites, and tunable surface terminations. However, the structure–property relationship of MXene composites remains highly nonlinear, and the design of high-efficiency absorbers still relies heavily on trial-and-error experiments. Herein, multidimensional magnetic components, including zero-dimensional (0D) Fe₃O₄ nanoparticles, one-dimensional (1D) Fe₃O₄/Co₃O₄ nanowires, and two-dimensional (2D) Fe₃O₄-based heterostructures, were rationally integrated with Fe/MXene and Fe/Co/MXene nanosheets to engineer synergistic dielectric and magnetic losses. Comprehensive electromagnetic characterization and loss mechanism analysis reveal that the structural dimensionality strongly impacts impedance matching and attenuation capability. To further enable predictive and data-driven optimization, a machine learning framework was established to correlate the microstructure, component ratio, thickness, and electromagnetic parameters with the microwave absorption performance (e.g., minimum reflection loss (RL_min), effective absorption bandwidth (EAB)). The optimized multidimensional composite achieves an RL_min of −56.4 dB at 10.2 GHz with an EAB of 8.4 GHz (9.6–18.0 GHz) at a thin matching thickness of 1.8 mm. The machine learning model demonstrates excellent accuracy (R² = 0.947) and enables the inverse design of absorber geometries to target specific operational frequencies. This work provides a generalizable paradigm for the intelligent design of MXene-based microwave absorbers and opens up broader opportunities for the AI-accelerated discovery of advanced electromagnetic functional materials.