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

Abstract

Approaching 27% of power-conversion efficiency and offering solution processability, perovskite solar cells (PSC) have paved a path to high-efficiency and cost-effective solar cell technologies. Despite huge potential, PSC's commercialization is hampered by low stability, J-V hysteresis, and grain boundaries-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 by 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 trappings, 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 degradations. For the modeling parameters inaccessible via experiments, a possibility to combine DD and Density Functional Theory (DFT) is explored. Furthermore, we present how machine learning models and interfacing experimental data can help speeding up and in improving 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.

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