The modernization of urban infrastructure and the rapid electrification of transportation networks have brought the severe energy inefficiency of conventional windows into sharp focus. Windows are among the least energy-efficient components, accounting for 50 to 75 percent of the thermal load in electric vehicle cabins and about 45 percent of total energy losses in buildings. In this push for a sustainable environment, dynamic smart windows are increasingly demanded to replace static commercial privacy glasses. However, current commercial glazing cannot adapt to seasonal environmental shifts and suffers from inherent physical trade-offs. Hence, they are far from efficient in many points of view, from all-weather energy conservation to thermal comfort.
In a new paper published in Light: Science & Applications , a team of scientists, led by Professor Yi Long from The Chinese University of Hong Kong, Professor Bin Hu from Huazhong University of Science and Technology, and Professor Shouqin Tian from Wuhan University of Technology, have developed an artificial intelligence-driven framework for the inverse design of function-oriented thermochromic smart windows. Based on a physics-guided neural network, they designed an energy-efficient window optimized for directional privacy protection. Featuring size-controlled vanadium dioxide nanoparticles, this smart window achieves ultrahigh simultaneous modulation of near-infrared transmittance and longwave infrared emissivity. The smart window allows for climate-adaptive all-weather thermal management without compromising the strict dark tint required for commercial privacy protection.
The intelligent smart window is centered around a four-layer Fabry-Perot resonator architecture, empowered by the machine learning algorithm. The photonic structure can be precisely tailored by tuning the geometric parameters of the vanadium dioxide nanoparticles and the underlying polymeric spacer. It can support massive dual-band optical modulation to adapt to various real-world climates. These scientists summarize the operational principle of their design:
“We develop a machine learning-assisted inverse design framework to navigate the complex and nonlinear relationship between optical performance, particle geometry, and multilayer configuration,” the scientists explained. “Our physics-guided neural network allows us to identify that reducing the vanadium dioxide nanoparticle size universally enhances the dual-band modulation, while the spacer thickness can be meticulously calibrated to maximize the optical resonance.”
“The directional privacy protection arises from a low photopic transmittance combined with exterior-interior luminance asymmetry.” they added.
“When applied to electric vehicles, our smart window exhibits robust passive thermal regulation, reducing the maximum indoor air temperature in the summer while preserving heat in the winter. This work establishes a generalizable route for future smart cities, zero-energy building envelopes, and advanced electric vehicles, offering significant energy savings across global mid-latitude regions,” the scientists forecast.
Light: Science & Applications
Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows