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.