ABSTRACT Interface passivation at the perovskite/electron‐transport‐layer (ETL) is key to reducing defects in perovskite solar cells (PSCs), yet the broad chemical space of passivators hinders discovery. Here, we present a machine‐learning (ML)‐guided workflow accelerating the identification of effective ammonium‐salt passivators for both n‐i‐p and p‐i‐n device architectures. A rigorously curated dataset of 296 literature records spanning 2017–2024, was stratified into 230 n‐i‐p and 66 p‐i‐n entries. Each passivator was represented via five molecular fingerprints and three descriptor sets, yielding eight distinct feature matrices per architecture. After training 72 regression models using tenfold cross‐validated RandomizedSearchCV, 256&XGBoost performed best for n‐i‐p (test MAE = 0.0431, RMSE = 0.0647), while RDKit&SVR excelled for p‐i‐n (test MAE = 0.0393, RMSE = 0.0516.). SHAP revealed that passivator concentration, molecular weight within 120–280 g mol −1 , surface polarity fragments, and halide composition, particularly I/Br ratio, as the principal drivers of PCE enhancement. Guided by these insights, virtual screening of 162 salts at 9 concentrations identified top candidates. To validate our predictions experimentally, N,N,N‐trimethyl‐N‐(3‐hydroxypropyl) ammonium iodide was applied to a Cs 0.05 FA 0.95 PbI 3 ‐based p‐i‐n device, yielding an improvement ratio of 1.152, closely matching the predicted value of 1.134 with a relative error of only 1.58%. Overall, this work establishes a transferable, SHAP‐guided and architecture‐aware machine‐learning framework that efficiently screens top interface passivators and provides physically meaningful design rules across different device configuPassivator, machine learning, perovskite solar cellrations.
Zhu et al. (Sat,) studied this question.