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AI just designed a material that cools buildings better than paint—and it could revolutionize energy savings.In a breakthrough study, scientists from the University of Texas at Austin used machine learning to engineer thermal meta-emitters—complex materials that selectively release heat. When tested under direct sunlight, roofs coated with these AI-designed materials stayed 5 to 20°C cooler than those painted with traditional white or gray coatings.The cooling could slash energy use by over 15,000 kilowatt-hours per year in hot cities like Rio or Bangkok—equivalent to powering 10 air conditioners for a year.But the impact goes beyond buildings. These materials could reduce urban heat, cool down cars, regulate spacecraft temperatures, and even be woven into fabrics for heat-resistant clothing. Their strength lies in their precision: they manage heat at targeted wavelengths, something traditional designs have struggled to achieve.Until now, creating such materials was slow and limited. AI broke through that barrier, generating over 1,500 unique structures with unprecedented performance.This marks a leap for passive cooling and sustainable design—where the future of temperature control could be built into the very materials around us.Follow Science Sphere for regular scientific updatesRESEARCH PAPER 📄Xiao et al, “Ultrabroadband and band-selective thermal meta-emitters by machine learning.”, Nature(2025)