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  1. Home
  3. Machine Learning Inverse Design Reveals A Double Narrow-band Absorption Approach For Effective Colored Radiative Cooling Paints.
  1. Home
  3. Machine Learning Inverse Design Reveals A Double Narrow-band Absorption Approach For Effective Colored Radiative Cooling Paints.

Related Experiment Video

Fabrication of Ultra-thin Color Films with Highly Absorbing Media Using Oblique Angle Deposition
06:30

Fabrication of Ultra-thin Color Films with Highly Absorbing Media Using Oblique Angle Deposition

Published on: August 29, 2017

Machine Learning Inverse Design Reveals a Double Narrow-Band Absorption Approach for Effective Colored Radiative

Ziqi Guo1,2, Dudong Feng1,2, Daniel Carne1,2

  • 1School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907-2088, United States.

Nano Letters
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed a machine-learning framework for designing colored radiative cooling paints. This innovative approach optimizes cooling performance and color, uncovering a novel "double narrow-band absorption" strategy for enhanced thermal management.

Keywords:
color sciencecolored radiative cooling paintsinverse designmachine learning

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Optical Engineering

Background:

  • Colored radiative cooling paints are crucial for aesthetic thermal management.
  • Previous designs were limited by cost, intuition, or idealized optical spectra.

Purpose of the Study:

  • To develop a machine-learning-enabled inverse design framework for optimal radiative cooling paints.
  • To bridge the gap between theoretical spectra and practical paint formulations.
  • To achieve desired color and high cooling performance simultaneously.

Main Methods:

  • Integration of photon Monte Carlo simulations with surrogate modeling.
  • Machine-learning-enabled inverse design framework.
  • Systematic exploration of the HSL color space.

Main Results:

  • Uncovered a "double narrow-band absorption" strategy for reduced solar heating.
  • Achieved reductions in solar heating power of up to 193 W/m² compared to conventional methods.
  • Determined that 24% of colors can achieve subambient cooling under realistic material constraints.

Conclusions:

  • The automated pipeline provides practical guidelines for designing high-performance, aesthetically tailored radiative cooling coatings.
  • Demonstrated the feasibility of achieving significant cooling with colored paints.
  • Highlights the potential of machine learning in materials design for thermal management.