AI-Designed Paints Reduce Building Temperatures by Up to 20 Degrees
Imagine painting the roof of your house with a special paint and suddenly saving thousands of kilowatt-hours of electricity per year because the building stays cooler even in the most intense heat. This is not science fiction, but reality thanks to new research that uses artificial intelligence to design advanced materials. Scientists from universities in the US, China, Singapore, and Sweden, including Yuebing Zheng from the University of Texas at Austin, Han Zhou from Shanghai Jiao Tong University, and Cheng-Wei Qiu from the National University of Singapore, have developed paints that reflect sunlight and radiate heat more efficiently than conventional coatings. This discovery, published in the prestigious journal Nature, promises to reduce energy consumption for air conditioning and mitigate the urban heat island effect, which causes temperatures in city centers to climb several degrees higher than on the outskirts.
How Artificial Intelligence Has Transformed Materials Design
Traditional methods of developing materials were based on trial and error, which meant endlessly testing different combinations. That is now changing thanks to machine learning, which automates the process known as inverse design. The scientists created a framework that explores a vast number of possible structures and materials, including three-dimensional metastructures. For example, they used a tri-plane modeling method that makes it possible to design complex 3D shapes instead of flat 2D surfaces. This approach enabled them to create seven prototype meta-emitters that outperform existing technologies in optical performance and cooling effect. According to Yuebing Zheng, designing a new material now takes only a few days instead of months because AI guides researchers toward the right structures and materials without lengthy testing.
The research showed that these paints can reduce building temperatures by 5 to 20 degrees Celsius after exposure to the midday sun. For example, applying such paint to a four-story apartment building in a hot climate such as Rio de Janeiro or Bangkok would save 15,800 kilowatt-hours of electricity per year. If applied to a thousand such buildings, the savings would be enough to power more than 10,000 air-conditioning units for an entire year. This represents enormous energy savings because the paints not only reflect solar radiation but also radiate heat in an ultra-broadband or band-selective mode, meaning that the way the material interacts with light at the nanoscale can be precisely tuned.
Applications from Buildings to Cars
These new paints are not just for the roofs of homes. Scientists propose using them on cars, trains, electrical equipment, or even in the aerospace industry, where cooling is crucial in an era of global warming. Potential applications include thermophotovoltaics (converting heat into electricity), thermal camouflage, and advanced optical devices. The research highlights how AI makes it possible to design materials with precisely defined properties—from broadband heat radiation to selective emissions at specific wavelengths. This opens the door to colored emitters or devices for quantum technologies.
Similar approaches are also emerging in other fields. For example, the British company MatNex used AI to create rare-earth-free permanent magnets for electric motors, reducing the carbon footprint of mining. Microsoft, meanwhile, released tools for the rapid design of inorganic materials for solar panels or medical implants. Scientists such as Alex Ganose from Imperial College London note that AI makes it possible to reverse the process: instead of testing a material's properties, you first define what you want, and AI designs a solution. This involves calculating millions of combinations, which was previously impossible due to limited computing power.
Challenges and the Future of the Technology
Although the research has overcome the limitations of traditional methods, such as local optimization traps or restrictions to predefined geometries, challenges remain. For example, manufacturing these 3D metastructures requires advanced technologies, but the scientists have already secured patents and developed software for further development. The team plans to extend this platform to the broader field of light-based nanotechnology, which could lead to better materials for carbon capture or more efficient batteries.
This discovery is not only about cooling—it is a step toward a more sustainable future. At a time when cities are suffering from extreme temperatures, these AI-designed paints could help reduce energy consumption and mitigate the impacts of climate change. If the technology becomes widespread, we could see cooler streets in Prague, London, or Shanghai, all thanks to the smart combination of science and artificial intelligence.
