Investigation of the optical and electronic properties of double perovskite Li2CuBiX6 (X = Br, I) for photovoltaic applications using first-principles and machine learning approaches

Abstract

The development of efficient and stable lead-free materials is essential for advancing next-generation photovoltaic technologies. In this study, we investigate Li₂CuBiX₆ (X = Br, I) double perovskites as promising absorber materials, using first-principles calculations and machine learning techniques. Density functional theory (DFT) results show indirect band gaps of 1.7 eV (Br) and 1.3 eV (I), suitable for solar energy conversion. Key optical properties, including absorption coefficient, reflectivity, refractive index and dielectric function, confirm their strong ability to capture light. A solar cell architecture FTO/ETL/Li₂CuBiX₆/HTL/Mo was modeled in SCAPS-1D, evaluating various electron and hole transport layers. SnS₂ and Cu₂O were identified as the best ETL and HTL, respectively, producing high energy conversion efficiencies of 27.24 % (Li₂CuBiBr₆) and 31.80 % (Li₂CuBiI₆). We also analyzed the effects of interfacial defects, doping concentration, absorber thickness and temperature on device performance. To predict efficiency trends and optimize configurations, we applied machine learning models (XGBoost, Random Forest, SVR). XGBoost achieved the highest accuracy, with R² = 99.87 % and a low RMSE. This work highlights the potential of Li₂CuBiX₆ as an efficient, lead-free solar absorber and demonstrates the value of combining first-principles simulations with machine learning for photovoltaic design.