Exploring device physics of perovskite solar cell via machine learning with limited samples

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外文摘要：Perovskite solar cells(PSCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833％and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercial applications.

外文关键词：

Perovskite solar cellMachine learningDevice physicsPerformance predictionLimited samples

作者：

Shanshan Zhao、Jie Wang、Zhongli Guo、Hongqiang Luo、Lihua Lu、Yuanyuan Tian、Zhuoying Jiang、Jing Zhang、Mengyu Chen、Lin Li、Cheng Li

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作者单位：

School of Electronic Science and Engineering,Xiamen University,Xiamen 361005,Fujian,China

Future Display Institute of Xiamen,Xiamen 361005,Fujian,China

基金：

National Key Research and Development ProgramNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Fujian Province of China

项目编号：

2022YFF0609504619741265190227362005230620014052021J06009

出版年：

2024

DOI：

10.1016/j.jechem.2024.03.003

能源化学

中国科学院大连化学物理研究所中国科学院成都有机化学研究所

能源化学

CSTPCDEI

影响因子：0.654

ISSN：2095-4956

年,卷(期)：2024.94(7)