AI-guided design of optoelectronic molecularly modified CH₃NH₃PbI₃ perovskite thin films with improved aqueous stability

The inherent vulnerability of halide perovskite films to moisture and water exposure severely restricts their stability, posing a challenge for industrial implementation. In this study, photoelectrochemical experiments, machine learning, and first-principles calculations are employed to accelerate the design toward aqueous-stable lead halide perovskite thin-film materials. A molecularly modified halide perovskite dataset incorporating diverse design parameters is constructed, enabling the development of a machine learning model that predicts a complex molecularly modified perovskite system that offers decent photocurrent in aqueous-based hostile environments. Specifically, a dye-modified MAPbI₃ material, with ethyl red deposited as a molecular modifier on top of the perovskite thin film and an equimolar precursor ratio (PbI₂: MAI = 1:1), is subsequently verified experimentally. The resulting CH₃NH₃PbI₃ film achieves an improved photocurrent in aqueous solution and an enhanced photogenerated current retention rate of 98.56% in water after 400 s. Density functional theory reveals the atomic-scale origins underlying the machine-learning-predicted system, including the intimate interfacial contact between the molecular adsorbate and the perovskite substrate, as well as the resulting optimal optoelectronic properties. This study underscores the critical role of molecular composition and processing conditions in enhancing the aqueous stability of halide perovskites and demonstrates an accurate data-driven framework that enables AI-accelerated stability prediction and materials design.