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

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original 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.

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