Machine Learning‐Driven Discovery and Experimental Validation of Effective Precursor Additives in Perovskite Solar Cells

Abstract

Rationally selection of precursor additives during perovskite crystallization is essential for obtaining high‐quality films and achieving high‐performance perovskite solar cells (PSCs). However, the discovery and optimization of effective additives still rely heavily on time‐consuming and costly trial‐and‐error experiments. In this work, we proposed a machine learning (ML) assisted screening strategy for precursor additives by integrating process parameters, material physicochemical properties, and molecular descriptors into a unified feature system. Among the five ML algorithms evaluated, the random forest model achieved the best predictive performance with relative errors below 5% on the external dataset. SHapley Additive exPlanations (SHAP) analysis further quantified the contribution of key features to device efficiency, offering guidance for additive structural design and property optimization. Following the established prescreening rules, two additives, [1,2,4]triazolo[1,5‐ a ]pyridine‐6‐carboxylic acid (6‐CATPy) and 4‐hydroxybenzenesulfonamide (4‐HBSA), were selected for experimental closed‐loop verification. Both additives effectively improved the preferred crystallization orientation, surface morphology, and defect passivation of perovskite films, resulting in enhanced film quality and power conversion efficiencies (PCEs) of 24.07% and 25.44%, respectively. These findings validate the proposed data‐driven screening strategy and demonstrate its potential for accelerating the rational discovery and optimization of precursor additives for high‐performance PSCs.