Machine learning-assisted discovery of Lewis base additives for defect passivation in perovskite solar cellsOA
Defect-induced nonradiative recombination critically restricts the power conversion efficiency(PCE)and stability of perovskite solar cells(PSCs).Lewis base additives show great promise in defect passivation,but current screening methods rely heavily on empirical trial and error and lack clear design principles,making it difficult to efficiently discover high-performance candidate materials.Here,we present a machine learning(ML)framework to intelligently screen Lewis base molecules for defect passivation.We trained six ensemble models on a dataset of 146 experimental data points,with Light Gradient Boosting Machine(LightGBM)yielding the best classification performance(87%accuracy).Shapley Additive Explanations(SHAP)interpretability analysis subsequently identifies the highest occupied molecular orbital(HOMO)energy(−7.5 to−6.3 eV),additive concentration(2.5 to 6.5 mg/mL),and simplified molecular backbones(O atom≤2,C atom<5)as critical design criteria.The ML prediction was experimentally validated:(S)-pyrrolidine-3-carboxylic acid((S)-PCA)and 2-methyl-1,3-cyclopentanedione(MCPD)(ClassⅡ)improved PCE by 2.22%and 2.01%,respectively,while 3-hydroxymethyl-3-methylbutanenitrile(3-HMBN)(ClassⅠ)showed minimal gain.Density functional theory(DFT)calculations further confirmed the stronger binding affinities and elevated defect formation energies of ClassⅡadditives.Notably,the champion(S)-PCA device achieved a PCE of 24.05%.This work established an ML-accelerated paradigm for the rational design of defect passivators,bridging data science and photovoltaics.
MENG Jiangtao;DING Bin;LUO Shulin;ZHANG Qi;LI Yuanliang;WU Shuangjia;ZHAO Zhiyu;WANG Gangcheng;LIU Jun;WU Guixuan;DING Yong;ZHANG Wenyuan
State Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,China University of Chinese Academy of Sciences,Beijing 100049,ChinaCollege of Chemistry,Chemical Engineering and Materials Science,Soochow University,Suzhou 215123,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,China University of Chinese Academy of Sciences,Beijing 100049,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,China University of Chinese Academy of Sciences,Beijing 100049,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,China University of Chinese Academy of Sciences,Beijing 100049,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,ChinaCollege of Renewable Energy,Hohai University,Changzhou 213000,ChinaState Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,China
信息技术与安全科学
machine learningperovskite solar cellsLewis basedefects passivationdensity functional theory
《燃料化学学报(中英文)》 2026 (7)
P.218-249,32
Supported by the Fundamental Research Program of Shanxi Province(202403021222487)the Foundation from Chinese Academy of Sciences(YBR2023001,YBR2025003)。
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