Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells

Abstract

Intelligently screening passivation materials is critical for improving the power conversion efficiency (PCE) values of perovskite solar cells (PSCs), which are still lacking. Herein, machine learning is employed to map the correlations between the PCE and interface passivation material at the atomic level, enabling intelligent material screening. The dataset includes around 100 interface materials used at the perovskite/hole transport layer interface. The random forest model best predicts the PCE, with a root mean square error of 0.7%. High-throughput predictions are further made and rationalized using density functional theory calculations. It is revealed that a material with a high binding energy with the [PbI₄]²⁻ surface promotes strong passivation effects, and small organic cations with an NH₃⁺ terminal have high potential. Experimental validation using methylammonium iodide and phenethylammonium iodide as the interface materials reveals the reliability of the predictions. Our work enables the high-throughput and rapid screening/design of interface materials for highly efficient PSCs, and it also provides general screening rules for interface materials at the atomic level.