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.