Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption

Inspired by the regulation mechanism of window blinds, this study designs an electromagnetic wave‐absorbing metamaterial. By introducing the magneto‐electric coupling concept and integrating it with an artificial intelligence‐based data‐driven collaborative optimization strategy, the material optimizes impedance matching performance and enhances loss capability while reducing its thickness. Within the ultra‐broad frequency range of 2.6–40 GHz, this material achieves excellent performance with a reflection loss below –10 dB. ABSTRACT The core challenge in developing lightweight and high‐efficiency electromagnetic wave absorbing materials lies in achieving a decoupled improvement in impedance matching and loss performance while reducing thickness. Inspired by the structure of window blinds, this study proposes and designs a Discrete Slat Tunable Electromagnetic Wave Absorption Material (DSTEAM). By incorporating a magneto‐electric coupling concept and an artificial intelligence‐assisted data‐driven optimization strategy, the successfully fabricated DSTEAM exhibits outstanding performance with a reflection loss below –10 dB over an ultra‐broadband frequency range of 2.6–40 GHz, while maintaining a thin thickness of only 9.85 mm and an areal density of 0.566 kg/m2. The superior performance of DSTEAM is attributed to gradient‐induced multiple scattering at discrete sheet interfaces, a synergistic enhancement of localized field strength, and a magneto‐electric coupling modulation mechanism. This AI‐driven collaborative design strategy offers a novel concept and effective pathway for the development of next‐generation lightweight and broadband electromagnetic wave absorption materials. Inspired by the regulation mechanism of window blinds, this study designs an electromagnetic wave-absorbing metamaterial. By introducing the magneto-electric coupling concept and integrating it with an artificial intelligence-based data-driven collaborative optimization strategy, the material optimizes impedance matching performance and enhances loss capability while reducing its thickness. Within the ultra-broad frequency range of 2.6–40 GHz, this material achieves excellent performance with a reflection loss below –10 dB. ABSTRACT The core challenge in developing lightweight and high-efficiency electromagnetic wave absorbing materials lies in achieving a decoupled improvement in impedance matching and loss performance while reducing thickness. Inspired by the structure of window blinds, this study proposes and designs a Discrete Slat Tunable Electromagnetic Wave Absorption Material (DSTEAM). By incorporating a magneto-electric coupling concept and an artificial intelligence-assisted data-driven optimization strategy, the successfully fabricated DSTEAM exhibits outstanding performance with a reflection loss below –10 dB over an ultra-broadband frequency range of 2.6–40 GHz, while maintaining a thin thickness of only 9.85 mm and an areal density of 0.566 kg/m 2. The superior performance of DSTEAM is attributed to gradient-induced multiple scattering at discrete sheet interfaces, a synergistic enhancement of localized field strength, and a magneto-electric coupling modulation mechanism. This AI-driven collaborative design strategy offers a novel concept and effective pathway for the development of next-generation lightweight and broadband electromagnetic wave absorption materials. Advanced Science, EarlyView.