AI-created paint keeps buildings up to 20C cooler and slashes energy bills

AI-Driven Paint Formulas to Keep Buildings Cooler and Cut Energy Use

Artificial intelligence is now accelerating materials science by helping develop innovative paint coatings that can keep buildings significantly cooler under the midday sun. These AI-engineered paints can reduce surface temperatures by 5°C to 20°C compared to conventional paints, potentially lowering the urban heat island effect in cities and cutting air-conditioning costs.

The research, conducted by teams at the University of Texas in Austin, Shanghai Jiao Tong University, the National University of Singapore, and Umeå University in Sweden, applied machine learning to design paint formulas optimized for reflecting sunlight and efficiently emitting heat.

How AI Enhances Paint Performance

Traditional material design often relies on trial-and-error experimentation, which can be time-consuming and limited in scope. Using machine learning, researchers expanded the design space and automated the search for optimal combinations of materials and structures. This approach drastically cuts the time to develop new coatings from months to days.

Professor Yuebing Zheng from the University of Texas, co-leader of the study, explained that the AI framework allows scientists to follow specific structural and material recommendations without multiple rounds of physical testing, enabling the creation of materials with properties previously thought impossible.

Practical Impact and Energy Savings

One of the study's scenarios estimated that applying these AI-designed paints to the roofs of a four-storey apartment block in hot climates like Rio de Janeiro or Bangkok could save approximately 15,800 kilowatt hours of electricity annually. Scaling this to 1,000 buildings could save enough electricity to power over 10,000 air conditioning units for a full year.

These coatings are not limited to buildings. They could also be applied to vehicles, trains, and electrical equipment—areas where heat management is increasingly critical as global temperatures rise.

Broader Trends in AI-Driven Materials Science

This paint innovation is part of a broader trend where AI expedites the discovery and optimization of advanced materials. For example:

• MatNex, a British company, used AI to develop permanent magnets for electric vehicle motors that avoid rare earth metals, reducing carbon-intensive mining.
• Microsoft has launched AI tools to accelerate the design of inorganic crystalline materials, which are integral to solar panels and medical implants.
• Researchers are employing AI to create better materials for carbon capture and more efficient batteries.

Dr Alex Ganose, a chemistry lecturer at Imperial College London, noted that AI enables exploration of millions of potential material combinations, overcoming previous computational limits. This allows scientists to specify desired properties upfront and let AI generate candidate materials, effectively reversing traditional material development workflows.

For professionals interested in how AI intersects with material science and other research areas, exploring targeted AI training can be valuable. Platforms like Complete AI Training offer courses on AI applications across various scientific fields.