Figure 1 . The overall scheme of using ML to assist the fabrication of PCBM-PSCs with nanopatterned TiO2 layer, where the process includes a) a collection of experimental data for PSCs with nanopatterned TiO2 layer and PCBM-PSCs, (b) predictions of Jsc , Voc ,ff and ECE for the PCBM-PSCs with nanopatterned TiO2 layer through machine learning, (c) the fabrication of PCBM-PSCs with nanopatterned TiO2 layer, (d) device characterization and (e) the conclusion for the results obtained.
Figure 1 shows the overall scheme of using ML to assist the fabrication of PCBM-PSCs with nanopatterned TiO2 layer. The present process involves the collection of experimental data with the Jsc , Voc , ffand ECE values for PSCs with nanopatterned TiO2 layer and PCBM-PSCs as listed in Table S1 and Table S2 in the supporting information. The nanopatterning depth of PSCs with nanopatterned TiO2 layer were varied to 70, 75, 80, 87, 92, 97, 102, 107, 112, 117, 122, 127, 132, 137, 142, 147, 152, 157 and 167 nm while the wt% of PCBM for PCBM-PSCs were varied to 0.01, 0.03, 0.05, 0.07, 0.10, 0.12 and 0.15 wt%. Subsequently, the dataset was used to predict the best combination of nanopatterning depth and the wt% of PCBM, as well as the Jsc ,Voc , ff and ECE of the PCBM-PSCs with nanopatterned TiO2 layers. To achieve that goal, a Random Forest (RF) regression model, which is an ensemble method that uses multiple decision trees to improve the prediction accuracy and prevent overfitting was used.[20] Then, the best combination of nanopatterning depth and wt% of PCBM that yielded the highest ECE was selected as a reference to fabricate the PCBM-PSCs with nanopatterned TiO2 layer. After the fabrication process, the device was characterized to further understand the dual effects of nanopatterning depth and wt% of PCBM on PSCs. Lastly, the conclusions were deduced based on the results obtained throughout the process.