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<p>In AI-driven metamaterials discovery, designing metasurfaces requires extrapolation to unexplored performance regimes to discover new structures. Here we introduce MetaAI, a physics-aware current-diffusion framework that synergizes spatial topologies and frequency-domain responses to discover non-intuitive metasurface architectures. Unlike conventional inverse design constrained by predefined specifications, MetaAI operates as a performance synthesizer by generating electrical current distributions that bridge electromagnetic performance and metasurface structures. This enables both in-distribution and out-of-distribution targets with diverse topologies. The core innovation of the proposed framework lies in its dual-domain diffusion module, which directly correlates meta-atom current mechanisms with electromagnetic behaviours to enable the discovery of structures with 17.2% wider operational bandwidths. We validate MetaAI across single-layer, multilayer and dynamically tunable metasurfaces, demonstrating out-of-distribution generalization across full-wave simulations and experimental prototypes.</p>

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Current-diffusion model for metasurface structure discoveries with spatial-frequency dynamics

  • 期刊论文

  • 发表日期：

    2025年12月31日

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  • Erji Li,
  • Yusong Wang,
  • Lei Jin,
  • Zheng Zong,
  • Enze Zhu,
  • Bao Wang,
  • Qian Wang,
  • Zongyin Yang,
  • Wen-Yan Yin,
  • Zhun Wei

摘要

In AI-driven metamaterials discovery, designing metasurfaces requires extrapolation to unexplored performance regimes to discover new structures. Here we introduce MetaAI, a physics-aware current-diffusion framework that synergizes spatial topologies and frequency-domain responses to discover non-intuitive metasurface architectures. Unlike conventional inverse design constrained by predefined specifications, MetaAI operates as a performance synthesizer by generating electrical current distributions that bridge electromagnetic performance and metasurface structures. This enables both in-distribution and out-of-distribution targets with diverse topologies. The core innovation of the proposed framework lies in its dual-domain diffusion module, which directly correlates meta-atom current mechanisms with electromagnetic behaviours to enable the discovery of structures with 17.2% wider operational bandwidths. We validate MetaAI across single-layer, multilayer and dynamically tunable metasurfaces, demonstrating out-of-distribution generalization across full-wave simulations and experimental prototypes.