Advancing the Design of High‐Efficiency Printable Hole‐Conductor‐Free Mesoscopic Perovskite Solar Cells Through Machine Learning

ABSTRACT

Breaking through the power conversion efficiency (PCE) limits of printable mesoscopic perovskite solar cells (p‐MPSCs) with machine learning (ML) shows great potential, but has not yet been accomplished. This work establishes a reliable workflow by constructing a high‐quality p‐MPSCs database for ML model development, followed by strategy formulation for achieving high‐performance p‐MPSCs. In the 8 validation experiments, the stacking ML model demonstrates excellent performance, with the prediction error not exceeding 2.16%. Model interpretability analysis reveals key factors influencing device performance and enables the formulation of screening rules for high‐quality precursor additives based on molecular fingerprinting. This validated framework guides the experimental realization of p‐MPSCs with a notable PCE of 19.36%, while theoretical projections suggest a maximum achievable efficiency of 24.32% through optimized design space exploration. A novel paradigm for accelerated discovery of p‐MPSCs is established through the synergistic integration of interpretable ML models and targeted experimental validation.