Abstract
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.
维普
万方数据