4.1.1. Prediction of the most optimal nanopatterning depth and
wt% of PCBM in PCBM-PSCs with nanopatterned TiO2 layer
The objective to predict Jsc ,Voc , ff and ECE of PCBM-PSCs with
nanopatterned TiO2 layer was formulated as a regression
problem and 108 datasets were used to train the ML model and another 12
datasets were used for validation. The data consists ofJsc , Voc , ff and
ECE for the PSCs with nanopatterned TiO2 layer and
PCBM-PSCs that were collected through previous experiments. In this
study, a Random Forest regression model was used to train the datasets.
Then, the multioutput regressor, which fits one regressor per target,
was opted to predict the Jsc ,Voc , ff and ECE, and Optuna was used for
hyperparameter tuning and obtaining the most optimal hyperparameter for
the model, as shown in the Table 5 below.
Table 5 . Optimal parameters for the ML model