Behold the paint job of the future—it doesn’t just look good, it keeps you cool and cuts your energy bill. In a breakthrough that blends nanoscience with machine learning, researchers in the United States, China, Sweden and Singapore have collaborated to develop a “smart paint” that can help keep buildings between 5 and 30 degrees Celsius cooler than standard paint.
AI helped engineer the coating at the nanoscale, designing materials that reflect sunlight and release heat instead of absorbing it. That simple but powerful shift could save households tens of thousands of kilowatt hours of electricity each year, drastically reduce air-conditioning costs, and even lower temperatures across entire urban areas.
“Our machine learning framework represents a significant leap forward in the design of thermal meta-emitters,” said Yuebing Zheng, a professor at the University of Texas at Austin’s Cockrell School of Engineering. “By automating the process and expanding the design space, we can create materials with superior performance that were previously unimaginable.”
The team includes researchers from Shanghai Jiao Tong University, the National University of Singapore and Umeå University in Sweden.
To build the paint, researchers worked with thermal metamaterials—engineered substances that can control how heat and light behave. By combining machine learning, computer modeling and hands-on lab testing, the team created a new family of high-performance coatings that could be produced at scale and applied to everything from rooftops and roads to vehicles and infrastructure.
It’s like sunscreen for your house—except smarter, and with a much broader range of potential uses. The paint could be applied to cars, trains, and even outdoor equipment—any surface that bakes in the sun and would benefit from staying cooler.
“To test their platform, the researchers fabricated four materials to verify the designs,” according to the Cockrell School of Engineering. “They further applied one of the materials to a model house and compared it to commercial paints on the cooling effect. After a four-hour midday exposure to direct sunlight, the meta-emitter–coated building roof came in between 5 and 20 degrees Celsius cooler on average than the ones with white and gray paints, respectively.”
The energy savings, they found, could be dramatic. “The researchers estimated that this level of cooling could save the equivalent of 15,800 kilowatts per year in an apartment building in a hot climate like Rio de Janeiro or Bangkok,” the school noted. “A typical air conditioning unit uses about 1,500 kilowatts annually.”
The applications go far beyond homes and offices. Using their machine learning framework, the researchers developed seven classes of thermal meta-emitters, each with different strengths and use cases. Some reflect sunlight. Others emit heat in narrow, targeted wavelengths. Some do both. Together, they offer a new toolkit for addressing one of the planet’s most pressing climate challenges: urban heat.
By coating buildings and roads with these materials, cities could reduce the so-called “urban heat island effect,” where concrete-heavy landscapes trap and radiate more heat than surrounding areas with vegetation. And in even more extreme environments, such as outer space, thermal meta-emitters could be used to manage spacecraft temperature by reflecting solar radiation and efficiently emitting heat.
And even beyond that, thermal meta-emitters could become ubiquitous—applied to clothes and outdoor equipment. Wrapping cars would not only protect the underlying paint job, but would keep the vehicle cool. And even inside the car, thermal meta-emitters could be embedded into interior materials to reduce the suffocating heat that builds up in parked vehicles.
“Machine learning may not be the solution to everything,” said researcher Kan Yao, “but the unique spectral requirements of thermal management make it particularly suitable for designing high-performance thermal emitters.”
The team is continuing to explore nanophotonics—the interaction of light and matter at the smallest scales. Combined with AI and metamaterials, this field is poised for exponential growth.
“Metamaterials are a class of artificially engineered materials with extraordinary properties not found in nature,” noted the researchers at Shanghai Jiao Tong University. “These materials exhibit unique characteristics and have wide-ranging applications in fields such as imaging, communications, energy, and aerospace.”
Clearly though, the results of this new type of paint point to the amazing ability of AI to do in a fraction of time what it would take a team of many researchers to do without AI. Without machine learning, according to the researchers, it would be like “groping through a maze in the dark, often relying on extensive experience and trial and error.”
“But everything changed with the introduction of AI into thermal radiative metamaterial design . . . Instead of tweaking existing recipes, the team developed an AI-powered inverse design model that breaks through the performance ceilings of current designs. The model can rapidly mass-generate high-quality candidate structure for thermal radiative metamaterials and identify the most engineering promising ones.”
