Computational Design of Efficient Lead-Free Double Perovskites for Photovoltaic Applications

Abstract

The global demand for sustainable energy is driving the search for efficient, non-toxic materials for next-generation optoelectronic and photovoltaic devices. This chapter first gives a state-of-the-art overview of perovskite materials, then presents a computational study of lead-free halide double perovskites Cs2NaInX6 (X = Br, I) using density functional theory (DFT). Structural, mechanical and thermodynamic stability are analysed via Goldschmidt's tolerance and octahedral factors, elastic constants and formation energies. Cs2NaInBr6 and Cs2NaInI6 are found to have direct band gaps (TB-mBJ) in the visible and near-IR ranges, respectively, identifying them as promising absorber layers. Their photovoltaic performance is assessed using the spectroscopic limited maximum efficiency model and SCAPS-1D device simulations, which highlight the strong potential of Cs2NaInI6 as an efficient lead-free absorber. The chapter concludes by outlining how machine learning and high-throughput screening could accelerate the discovery and optimisation of related materials for energy applications.