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

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

机构地区：[1]College of New Energy,Xi'an Shiyou University,Xi'an 710065,China [2]Frontiers Science Center for Flexible Electronics,Xi'an Institute of Flexible Electronics(IFE),Northwestern Polytechnical University,Xi'an 710072,China [3]Chongqing Innovation Center,Northwestern Polytechnical University,Chongqing 401135,China [4]The School of Information and Communications Engineering,Xi'an Jiaotong University,Xi'an 710049,China. [5]Key Laboratory of Flexible Electronics(KLOFE)&Institution of Advanced Materials(IAM),Nanjing Tech University,Nanjing 211816,China

出　　处：《Rare Metals》2024年第11期5720-5733,共14页稀有金属（英文版）

基　　金：financially supported by the National Natural Science Foundation of China(Nos.52202300,52372226,51972172 and 1705102);China Postdoctoral Science Foundation(No.2022M722591);the Natural Science Basic Research Plan in Shaanxi Province of China(Nos.2023-JC-QN-0643 and 2022JQ-629);the Fundamental Research Funds for the Central Universities;the Natural Science Foundation of Chongqing China(No.2023NSCQ-MSX0097)。

摘　　要：Eco-friendly lead-free tin(Sn)-based perovskites have drawn much attention in the field of photovoltaic s,and the highest power conversion efficiency(PCE)of Sn-based perovskite solar cells(PSCs)has been recently approaching 15%.However,the PCE improvement 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 predict 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 experimental data.Moreover,by analyzing the physical mechanisms 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.

关 键 词：Sn-based perovskite Machine learning Solar cells Wide bandgap Energy band alignment 

分 类 号：TM914.4[电气工程—电力电子与电力传动] TB34[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]