Genetic algorithm: Design of perovskite solar cells with broadband light-absorption optimization

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Chinese explanation / 中文解读

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Original abstract

Enhancing the light absorption of perovskite solar cells (PSCs) is essential for further improving their power conversion efficiency (PCE). In this work, a hierarchical broadband design framework for PSCs is proposed, in which GA-assisted optical optimization is combined with subsequent electrical evaluation. By exploiting the global search capability of GA, the multilayer architecture of PSCs can be systematically optimized. First, for a planar six-layer stack, the average optical absorptance is taken as the optimization objective, and the thickness of each layer is automatically optimized using GA to obtain a structure with enhanced optical performance. This approach efficiently explores the multidimensional design space and identifies the optimal thickness combination through selection, crossover, and mutation operations. Furthermore, based on the optimized planar structure, a TiO2 grating is introduced into the ITO layer, and Au nanoparticles (Au NPs) are embedded in the perovskite absorber. By jointly optimizing the grating parameters and nanoparticle radius, light absorption is further improved. Energy-conservation analysis based on the finite-difference time-domain (FDTD) method shows that the optimized structure achieves an average absorptance of approximately 95% over the wavelength range of 300–800 nm. Compared with the unoptimized structure, the short-circuit current density (Jsc) increases by about 4.3085 mA cm⁻², and the PCE reaches approximately 25.86%. This work provides a promising strategy for the structural design of high-efficiency PSCs by combining optical optimization with subsequent optoelectrical evaluation, while also demonstrating the effectiveness of genetic algorithms in multiscale optoelectronic design.

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