首页|Machine learning guided efficiency improvement for Sn-based perovskite solar cells with efficiency exceeding 20％

Machine learning guided efficiency improvement for Sn-based perovskite solar cells with efficiency exceeding 20％

扫码查看
原文链接
  • 万方数据
  • 维普
Eco-friendly lead-free tin(Sn)-based per-ovskites have drawn much attention in the field of photo-voltaics,and the highest power conversion efficiency(PCE)of Sn-based perovskite solar cells(PSCs)has been recently approaching 15％.However,the PCE improve-ment of Sn-based PSCs has reached bottleneck,and an unambiguous guidance beyond traditional trial-and-error process is highly desired for further boosting their PCE.In this work,machine learning(ML)approach based on artificial neural network(ANN)algorithm is adopted to guide the development of Sn-based PSCs by learning from currently available data.Two models are designed to pre-dict the bandgap of newly designed Sn-based perovskites and photovoltaic performance trends of the PSCs,and the practicability of the models are verified by real experi-mental data.Moreover,by analyzing the physical mecha-nisms behind the predicted trends,the typical characteristics of Sn-based perovskites can be derived even no relevant inputs are provided,demonstrating the robustness of the developed models.Based on the models,it is predicted that wide bandgap Sn-based PSCs with optimized interfacial energy level alignment could obtain promising PCE breaking 20％.At last,critical suggestions for future development of Sn-based PSCs are provided.This work opens a new avenue for guiding and promoting the development of high-performing Sn-based PSCs.

Sn-based perovskiteMachine learningSolar cellsWide bandgapEnergy band alignment

Wei-Yin Gao、Chen-Xin Ran、Liang Zhao、He Dong、Wang-Yue Li、Zhao-Qi Gao、Ying-Dong Xia、Hai Huang、Yong-Hua Chen

展开 >

College of New Energy,Xi'an Shiyou University,Xi'an 710065,China

Frontiers Science Center for Flexible Electronics,Xi'an Institute of Flexible Electronics(IFE),Northwestern Polytechnical University,Xi'an 710072,China

Chongqing Innovation Center,Northwestern Polytechnical University,Chongqing 401135,China

The School of Information and Communications Engineering,Xi'an Jiaotong University,Xi'an 710049,China

Key Laboratory of Flexible Electronics(KLOFE)& Institution of Advanced Materials(IAM),Nanjing Tech University,Nanjing 211816,China

展开 >

2024

10.1007/s12598-024-02775-w

稀有金属(英文版)
中国有色金属学会

稀有金属(英文版)

CSTPCDEI
影响因子：0.801
ISSN：1001-0521
年,卷(期)：2024.43(11)