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Deep Reinforcement Learning-Guided Inverse Design of Transparent Heat Mirror Film for Broadband Spectral Selectivity.

Zhi Zeng¹, Haining Ji¹, Tianjian Xiao¹

¹School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China.

Materials (Basel, Switzerland)

|June 27, 2025

Summary

Deep reinforcement learning optimized transparent heat mirror films for windows. The novel Ta2O5/Ag/Ta2O5/Ag/Ta2O5 film design significantly reduces building energy consumption, achieving up to 17.93% savings in hot climates.

Keywords:

building energy efficiency deep reinforcement learning inverse design spectrally selective properties transparent heat mirror

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Area of Science:

Materials Science
Building Physics
Optics

Background:

Building energy consumption is rising, necessitating energy-efficient solutions.
Transparent heat mirror films enhance building energy efficiency through spectrally selective properties.
Traditional film optimization methods often focus on single parameters, limiting performance potential.

Purpose of the Study:

To develop an advanced method for optimizing transparent heat mirror films.
To simultaneously optimize thin-film material systems and layer thickness parameters.
To design a film with broadband spectral selectivity for enhanced energy efficiency.

Main Methods:

Utilized a deep reinforcement learning-based approach with an adaptive strategy network.
Employed inverse design to determine optimal film structure and layer thicknesses.
Investigated a Ta2O5/Ag/Ta2O5/Ag/Ta2O5 thin-film structure.

Main Results:

Achieved broadband spectral selectivity with a Ta2O5/Ag/Ta2O5/Ag/Ta2O5 (42 nm/22 nm/79 nm/22 nm/40 nm) film.
The film demonstrated high UV reflectance (75.5%), high near-infrared reflectance (93.2%), high visible transmittance (87.0%), and low mid- to far-infrared emissivity (1.7%).
Simulations showed maximum energy savings of 17.93% in hot climates.

Conclusions:

The designed transparent heat mirror film offers a viable strategy for reducing building energy consumption.
The deep reinforcement learning approach enables simultaneous optimization of material and thickness parameters.
The film exhibits excellent optical performance across various incident angles, suitable for complex lighting environments.