Drift-diffusion modeling of perovskite solar cells: past and future possibilities

Abstract

Approaching 27% power-conversion efficiency and offering solution processability, perovskite solar cells (PSCs) have paved the way for high-efficiency and cost-effective solar cell technologies. Despite huge potential, the commercialization of PSCs is hampered by low stability, J–V hysteresis, and grain boundary-led performance degradation. Drift-diffusion (DD) modeling has become an indispensable tool for investigating underlying device physics and various dynamical phenomena that are difficult to understand solely using experimental techniques. However, most of the proposed DD models rely on oversimplified assumptions and approximations and therefore do not mimic the actual device while modeling the role of interfaces, doping, mobilities, ionic migration, device architecture, and J–V hysteresis. Moreover, a significant gap remains in modeling short-term and long-term performance degradation. This review critically examines the evolution of DD modeling in PSCs, highlighting its strengths, limitations, and opportunities for improvement. We discuss strategies to enhance model accuracy by incorporating advanced sub-models for degradation, ionic trapping, mobility, grain boundaries, photon recycling, and quantum effects. We emphasize the incorporation of generation/annihilation of ionic defects and combining time/frequency domain analysis to predict short- and long-term performance degradation. For modeling parameters inaccessible via experiments, the possibility of combining DD and Density Functional Theory (DFT) is explored. Furthermore, we present how machine learning models and interfacing experimental data can help speed up and improve the accuracy and reliability of DD models. By identifying current gaps and proposing future directions, this review aims to guide the development of robust, scalable, and physically grounded DD models for PSCs.