Figure 3 . Predictions of (a) Jsc , (b)Voc , (c) ff and (d) ECE of PCBM-PSCs with nanopatterned TiO2 layers by using ML.
Next, the RF model was further employed to predict the best combination of nanopatterning depth and wt% of PCBM for Jsc ,Voc , ff and ECE of PCBM-PSCs with nanopatterned TiO2 as shown in Figure 3 . The model was trained using the Jsc ,Voc , ff , ECE, nanopatterning depths and wt% of PCBM data of PSCs with nanopatterned TiO2 layer and PCBM-PSCs, that were obtained from previous experiments. The results demonstrated that the highest predicted ECE of 16.73% was achieved with a combination of 127 nm nanopatterning depth and 0.1 wt% of PCBM. The top ten predicted results are also tabulated in Table 1 . Based on these findings, the best combination of nanopatterning depth and wt% of PCBM were used as a reference to fabricate PCBM-PSCs with nanopatterned TiO2 layer.
Table 1 . Top 10 prediction for the PCBM-PSCs with nanopatterned TiO2 layer by using ML.