Synergistic machine learning and DFT screening strategy: Accelerating discovery of efficient perovskite passivators

Efficient surface passivation is critical for achieving high-performance perovskite solar cells (PSCs), yet the discovery of optimal passivators remains a time-consuming, trial-and-error process. Here, we report a synergistic machine learning (ML) and density functional theory (DFT) approach that enables predictive and rapid identification of effective passivation materials. By training an XGBoost model (91.3 % accuracy) with DFT-derived molecular descriptors and activity calculations, we identify 2-(4-aminophenyl)-3H-benzimidazol-5-amine (APBIA) as a promising passivator. Experimental validation demonstrates that APBIA effectively removes surface impurities and passivates defects within perovskite films, leading to a significant increase in power conversion efficiency (PCE) from 22.48 % to 25.55 % (certified as 25.02 %). This ML-DFT framework provides a generalizable pathway for accelerating the development of advanced functional materials for photovoltaic applications.