Generative model for information metamaterial design

Information metamaterials are digital coding electromagnetic structures that connect wave control with information processing, offering programmable routes to beam shaping, focusing and holographic imaging. Their inverse design remains challenging because both meta-atoms and spatial coding arrays must be selected from a vast combinatorial space, and existing optimization or learning methods are often tailored to specific tasks, bit resolutions or field patterns. Here we show a generative model for information metamaterial design that learns a shared design prior and transfers it across diverse electromagnetic functions. The model combines a pretrained diffusion backbone with lightweight functionality-oriented adapters, enabling the generation of multibit meta-atoms with prescribed responses and nonuniform arrays for beam steering, near-field focusing and holography. Numerical simulations and experiments validate high-performance meta-atoms and functional 1-bit and 3-bit meta-arrays. For holographic design, the model reaches Gerchberg-Saxton-level fidelity while reducing runtime by over three orders of magnitude, establishing a scalable route to information-metamaterial discovery.