Generative AI‐Enabled Discovery and Experimental Demonstration of Ultra‐Broadband Epsilon‐Near‐Zero Perfect Absorbers

ABSTRACT

Artificial intelligence is reshaping the discovery and engineering of photonic materials. Here, we introduce a data‐driven framework that employs a generative deep neural network to create ultrathin, multilayer epsilon‐near‐zero absorbers with record‐breaking broadband performance. Within this framework, the neural network learns a generative distribution over high‐performing multilayer designs, optimizing the refractive index, layer arrangement, and thickness for maximal broadband absorption. Guided by AI, we experimentally demonstrate an optimized photonic structure that achieves near‐perfect light absorption (exceeding 98%) across more than 1000 nm bandwidth in the near‐infrared range, while remaining under 160 nm thick. Our approach demonstrates the power of integrating advanced computational algorithms with state‐of‐the‐art fabrication technology, offering a scalable route to high‐efficiency nanophotonic devices for target applications. Altogether, this work establishes a generalizable strategy for algorithm‐driven photonic design with implications across energy, sensing, and integrated optics.