Highly efficient graphene-based circular ring metasurface absorber for photonics device component design using artificial intelligence optimization

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This research introduces an advanced solar thermal absorber configuration utilizing graphene-based metasurfaces combined with cylindrical resonators on a silicon dioxide platform. The proposed architecture delivers outstanding broadband thermal absorption across the solar spectrum (0.2–3 μm), attaining an average efficiency of 96.415%. Finite element analysis confirms multiple resonance peaks surpassing 99% efficiency, with absorption bandwidths extending up to 2500 nm and maintaining levels above 98%, with spectral performance distributed as 77.8% in the UV region, 95.5% in the visible range, 98.3% in the near-infrared, and a remarkable 99.943% in the mid-infrared. Examination of parametric investigations highlight the influence of resonator geometry, incident light angle, and graphene's chemical potential. With a compact thickness of just 1400 nm, the proposed thermal absorber outperforms many existing designs in terms of broadband efficiency and angular stability. Additionally, the integration of a deep learning neural network model ensures reliable predictive accuracy, demonstrated by consistently high R 2 scores across various test scenarios. Altogether, the synergy of exceptional broadband thermal absorption, compact geometry, angular resilience, and machine learning integration establishes this solar absorber as a promising advancement in next-generation solar thermal energy harvesting technologies.