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  1. Home
  3. Machine Learning-driven Cooling Window Design Beyond Hyperbolic Metamaterials.
  1. Home
  3. Machine Learning-driven Cooling Window Design Beyond Hyperbolic Metamaterials.

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Machine Learning-Driven Cooling Window Design Beyond Hyperbolic Metamaterials.

Seok-Beom Seo1, Ye-Rin Choi1, Jong-Goog Lee1

  • 1Department of Applied Physics Kyung Hee University Yongin South Korea.

Nanophotonics (Berlin, Germany)
|March 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning (ML) inverse design created superior ultrathin cooling-window coatings. ML-optimized multilayers outperformed traditional designs, offering high visible transmittance and near-infrared reflectance for energy savings.

Keywords:
energy savinghyperbolic metamaterialmachine learning outperformancemetal/dielectric multilayeroptical coatingpassive cooling

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Area of Science:

  • Materials Science
  • Optics
  • Computational Science

Background:

  • Analytical multilayer designs are limited to narrow spectral bands.
  • Machine learning (ML) offers potential for optimizing multilayers across multiple bands.
  • The performance of ML-optimized multilayers versus analytical designs under identical constraints is largely unproven.

Purpose of the Study:

  • To experimentally validate the superiority of ML-driven inverse design for multilayer coatings.
  • To develop a cooling-window coating with high average visible transmittance (AVT) and high average near-infrared reflectance (ANR).
  • To compare ML-optimized aperiodic designs with periodic hyperbolic metamaterial (HMM) counterparts.

Main Methods:

  • Utilized a factorization machine integrated with simulated annealing for ML-driven inverse design.
  • Designed and fabricated ZnS/Ag multilayers.
  • Benchmarked ML designs against periodic hyperbolic metamaterial (HMM) structures.

    • Main Results:

    • ML-designed coatings achieved superior performance (0.57 AVT, 0.98 ANR) compared to HMMs (0.49 AVT, 0.83 ANR) under a 156 nm thickness constraint.
    • An extended ML design (300 nm) reached 0.79 AVT and 0.97 ANR by suppressing Fabry-Perot resonances.
    • ML-driven multilayers demonstrated tunable transmitted colors across the visible spectrum, unlike HMMs.

    Conclusions:

    • ML-driven inverse design is a powerful method for creating high-performance, ultrathin, and color-tunable cooling-window coatings.
    • These advanced coatings offer significant potential for urban energy savings.
    • The study experimentally confirms the advantage of ML optimization over traditional analytical methods for multilayer optical coatings.