Researchers report that they have used advanced computing technology and artificial intelligence to design a transparent window coating that could lower the temperature inside buildings, “without expending a single watt of energy.”

The news comes as concerns about increases in energy and material costs ripple around the world. Saint-Gobain, for example, said recently that its energy and raw material costs increased by nearly $3 billion in 2022. It added that it expects the costs of energy alone to amount to nearly $2.5 billion.

Marc LaFrance, a windows technology manager at the Department of Energy (DOE) says that 45% of the heat loss of the building envelope is associated with windows. To mitigate that loss, a DOE research and development report states that the adoption of dynamic and static technologies would substantially reduce peak electricity demand from buildings. These technologies have the potential to reduce U.S. annual energy use by 1.7% and CO2 emissions by 1.9% in 2050.

Dynamic glass is one such technology that helps reduce energy loss. The smart glass not only offers solar control but also provides savings on energy, maintenance and human-related costs. It achieves this by actively or passively controlling how much heat and sunlight a building receives. The glass opacity changes to reduce the amount of light and heat allowed to pass through.

The U.S. government has worked to make the adoption of dynamic glass easier thanks to the Inflation Reduction Act, which includes a new tax credit that covers up to 30% of the costs of dynamic glass.

Researchers from the University of Notre Dame and Kyung Hee University set out to go even further with a “transparent radiative cooler” (TRC) that could block ultraviolet and near-infrared light without impeding visibility.

According to the researchers’ paper, “High-Performance Transparent Radiative Cooler Designed by Quantum Computing,” published in ACS Energy Letters, their clear window coating design was a success.

The team writes that it constructed computer models of TRC consisting of alternating thin layers of common materials like silicon dioxide, silicon nitride, aluminum oxide or titanium dioxide on a glass base, topped with a film of polydimethylsiloxane. They optimized the type, order and combination of layers using an iterative approach guided by machine learning and quantum computing, which stores data using subatomic particles.

This computing method carries out optimization faster and better than conventional computers because it can efficiently test all possible combinations in a fraction of a second. This produced a coating design that, when fabricated, beat the performance of conventionally designed TRCs in addition to one of the best commercial heat-reduction glasses on the market.

The researchers state that the average annual energy savings in surveyed U.S. cities are 50 MJ/m2 if the TRC is used on windows. In cities with hot, dry weather, the TRC can potentially save around 86.3 MJ/m2 per year, or 31% of the cooling energy consumption when conventional windows are used.

They also did the same calculations for selected major cities worldwide. While buildings in all cities could benefit, those in tropical regions would get a greater cooling impact.