Machine Learning‐Optimized Electromagnetic Wave Absorption in Metal/C Nanocomposites

ABSTRACT Traditional carbon‐based electromagnetic wave absorbers suffer from limited tunability due to the intricate coupling of multiple synthesis parameters, hindering the rational design of high‐performance materials. Herein, we apply a genetic algorithm (GA) to optimize electromagnetic wave absorption (EWA) performance in Metal/C Nanocomposites. Over three generations of GA evolution, five key synthesis parameters—carbon precursor type, metal type, molar ratio of carbon precursor to metal ions, carbonization temperature, and filler loading ratio (wt.%)—are simultaneously tuned. Progressive optimization enhances the Enhanced Absorption Band (EAB) from an initial average of 1.24 to 4.08 GHz, while the minimal reflection loss (RL min ) improves from −20.29 to −41.9 dB. The champion sample achieves a remarkable RL min of −25.9 dB at 7.04 GHz with an EAB of 7.56 GHz. Random Forest and XGBoost models further quantify parameter importance, consistently identifying carbon precursor type (32.5% and 31.4%) and filler loading ratio (33% and 38.4%) as the dominant factors—validating the GA‐driven optimization pathway. This work demonstrates the potential of evolutionary algorithms in materials design and provides a transferable framework for high‐performance EWA materials.