Machine Learning-Assisted Fabrication of PCBM-Perovskite Solar Cells with Nanopatterned TiO_(2)Layer

To unlock the full potential of PSCs,machine learning(ML)was implemented in this research to predict the optimal combination of mesoporous-titanium dioxide(mp-TiO_(2))and weight percentage(wt%)of phenyl-C_(61)-butyric acid methyl ester(PCBM),along with the current density(J_(sc)),open-circuit voltage(V_(oc)),fill factor(ff),and energy conversion efficiency(ECE).Then,the combination that yielded the highest predicted ECE was selected as a reference to fabricate PCBM-PSCs with nanopatterned TiO_(2)layer.Subsequently,the PCBM-PSCs with nanopatterned TiO_(2)layers were fabricated and characterized to further understand the effects of nanopatterning depth and wt%of PCBM on PSCs.Experimentally,the highest ECE of 17.338%is achieved at 127 nm nanopatterning depth and 0.10 wt%of PCBM,where the J_(sc),V_(oc),and ff are 22.877 mA cm^(-2),0.963 V,and 0.787,respectively.The measured J_(sc),V_(oc),ff,and ECE values show consistencies with the ML prediction.Hence,these findings not only revealed the potential of ML to be used as a preliminary investigation to navigate the research of PSCs but also highlighted that nanopatterning depth has a significant impact on J_(sc),and the incorporation of PCBM on perovskite layer influenced the V_(oc)and ff,which further boosted the performance of PSCs.