A window coating can help reduce power bills and carbon emissions

Thanks to artificial intelligence and quantum computing.
Ameya Paleja
The clear coating on a glass pane on the left hand side.
The clear coating on a glass pane on the left hand side.

ACS Energy Letters  

A collaboration of researchers working across the seas has led to the development of a clear window coating that can cool down buildings without spending any energy, a press release said. The coating will come in handy as the world tries to cope with rising global temperatures and reduce carbon emissions.

Even as the focus of climate scientists is currently on transportation, factories, and construction activities, the major contributor to global emissions, cooling down buildings, also accounts for a significant portion of global energy consumption.

As much as 15 percent of the energy generated is spent on making homes and office spaces comfortable for occupation in the summers. Since fossil fuels are major sources of power across the globe, reducing energy consumption would also be an effective way to reduce emissions.

A window coating that helps cool the room

The light received from the sun comprises three types of wavelengths. Those in the visible spectrum that our eyes use to see objects around us as well as those in the ultraviolet and infrared range that is invisible to the human eye.

The infrared wavelength of light is well known to carry heat along with it, while the ultraviolet wavelength can be absorbed by objects, which heats them up. Since these wavelengths pass through a regular glass pane installed in rooms, they increase the temperature of the room. A window coating that can block these wavelengths could effectively help cool down the room.

Research teams led by Eungkyu Lee at Kyung Hee University in South Korea and Tengfei Luo at the University of Notre Dame in the U.S. collaborated to develop a window coating that was clear enough to let visible light in and not interfere with the view but still reflect the other wavelengths. Such coatings are usually referred to by the scientific community as a transparent radiative cooler (TRC).

Artificial Intelligence to compute ingredients

Instead of reinventing the wheel and looking for a new material that could radically help reduce temperatures, the research team turned to commonly available materials that could be used to make the TRC.

Using machine learning and quantum computing, they optimized the type, order, and combination of layers of commonly available materials like silicon dioxide, silicon nitride, and aluminum oxide to be put on the glass base to achieve the desired effect.

Advanced computing methods helped the team test all possible combinations very rapidly and by adding a film of polydimethylsiloxane on top of the coating, the team was able to produce a new design that beat conventional TRC designs comprehensively, the press release said.

The new TRC design, when put on conventional windows, could help reduce the energy consumption required for cooling in hot cities by as much as 31 percent. Additionally, the coating could also be applied on cars and trucks that also spend energy cooling their inner environments.

The research findings were published today in the journal ACS Energy Letters


Transparent radiative coolers can be used as window materials to reduce cooling energy needs for buildings and automobiles, which may contribute significantly to addressing climate change challenges. However, it is difficult to achieve high visible transparency and radiative cooling performance simultaneously. Here, we design a visually transparent radiative cooler on the basis of layered photonic structures using a quantum computing-assisted active learning scheme, which combines active data production, machine learning, and quantum annealing in an iterative loop. We experimentally fabricate the designed cooler and demonstrate its cooling effect. This cooler may lead to an annual energy saving of up to 86.3 MJ/m2 in hot climates compared with normal glass windows. The quantum annealing-assisted active learning scheme may be generalized for the design of other complex materials.

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