Optimization of Metal-Oxide Spectral Filters for Reduced Photovoltaic Heating via Machine-Learning Guided Thickness Design.

Infrared (IR)-induced heating in photovoltaic (PV) systems is a critical challenge that lowers efficiency and accelerates module degradation. In this work, we propose a multilayer thin-film IR filter (TiO2 (50 nm)/NiO x (100 nm)/Ag (various thickness)) as a reduced heating solution integrated with PV modules. The filter is designed to transmit visible light while reflecting IR radiation, thereby reducing thermal load without sacrificing photovoltaic current. We incorporate machine learning models specifically Gaussian Process Regression (GPR) to optimize the Ag layer thickness for maximum performance. The AI models are trained on a combination of experimental optical data, enabling efficient exploration of the thickness-performance space. Key findings demonstrate that an optimized Ag thickness (∼10 nm) yields high IR reflectance (over 50% in the 750-1200 nm range) while maintaining sufficient visible transmittance (>50%). Silicon solar cells with this filter showed improved performance: the short-circuit current and power output increased due to reduced thermalization losses, translating to a ∼2-3 °C drop in operating temperature and corresponding efficiency gains. These improvements can extend PV module lifespan and energy yield. Our results indicate that the TiO2/NiO x /Ag filter can be manufactured via scalable e-beam evaporation, and the integration of AI optimization accelerates the design of such photonic coatings. The demonstrated performance enhancements and the low-cost, scalable nature of the solution highlight strong potential for commercial deployment in extending PV module longevity. We adopt Gaussian Process Regression (GPR) as a surrogate tailored to small, high-fidelity experimental data sets. GPR provides calibrated predictive uncertainty and smooth, physics-consistent interpolation across film thickness and wavelength, enabling uncertainty-aware selection of robust thicknesses with few experiments. This contrasts with polynomial/linear fits (insufficiently expressive) and black-box models without calibrated uncertainty.