Haifeng Li, Yue Zang, Zhikang Zhu, Chenyang Zhu, Weihong Liu, Zihao Zhang, Wensheng Yan
Self-assembled monolayers (SAMs) have emerged as a new generation of hole transport materials (HTMs) for perovskite solar cells (PSCs), particularly in inverted architectures. Compared to conventional HTMs, SAMs demonstrate superior power conversion efficiency (PCE) and enhanced operational stability. However, the current discovery of SAMs still relies heavily on empirical trial-and-error approaches, suffering from long development cycles, high costs, and low success rates. Here we present a novel machine learning (ML) platform for accelerated SAM discovery and design. We constructed a comprehensive feature space combining RDKit molecular descriptors and Morgan fingerprints, and then systematically evaluated various ML algorithms. Multiple evaluation metrics were used to assess model reliability. The results demonstrate that the RDKit-based XGBoost model achieved optimal performance with a root-mean-square error (RMSE) of 1.862, a coefficient of determination ( R 2 ) of 0.5058, a Pearson correlation coefficient ( r ) of 0.8161 and a mean absolute error (MAE) of 1.528. Then, SHapley Additive exPlanations (SHAP) analysis further elucidated the structure–property relationships between key molecular features and photovoltaic performance. The SHAP values revealed that the top five most important features were all RDKit descriptors, specifically EState_VSA5, fr_benzene, EState_VSA2, SlogP_VSA1, and Chi0v. The external validation using recently reported SAM molecules demonstrated remarkable prediction accuracy. The relative errors between predicted and experimental PCE values were mostly within 10%, with the minimum being only 0.55%. Meanwhile, three new SAM molecules were designed based on the model, with the highest predicted PCE approaching 27%. Therefore, this work provides an efficient digital solution for SAM development, offering valuable guidance for accelerating the discovery of next-generation photovoltaic materials.