3. Conclusion
In conclusion, ML has been successfully implemented in this research to
integrate different factors and predict the best combination of
nanopatterning depth and wt% of PCBM. The best combination predicted
were at 127 nm nanopatterning depth and 0.10 wt% of PCBM withJsc , Voc , ff and
ECE values of 23.162 mA/cm2, 0.953 V, 0.753 and
16.727%. As the predicted performance of PCBM-PSCs with nanopatterned
TiO2 layer were higher than the PSCs with nanopatterned
TiO2 layer and PCBM-PSCs, the results proved that ML
were capable to learn the relationship between two different factors and
predict the performance of new PSCs. Therefore, with the assistance of
ML, PCBM-PSCs with nanopatterned TiO2 layer were
fabricated and characterized. The consistencies of experimental results
with the predicted values further validate the reliability of the ML
model. Through this approach, it has been observed that nanopatterning
depth significantly increased the Jsc , attributed
to the improved light harvesting through nanopatterning. The
nanopatterned TiO2 layer was able to enhance electron
generation, which leads to a higher electron density and thus increases
the Jsc . Furthermore, the incorporation of PCBM
into the perovskite layer enhanced the electron transport property of
PSCs, yielding to the increment in Voc andff . This is because PCBM were able to fill in the pinholes and
grain boundaries between the perovskites, resulting in a formation of
larger perovskite-PCBM grain sizes. Experimentally, the highest ECE
obtained was 17.336%, which represented a 32.5% improvement compared
to the pristine PSC. These results pave the way to understand the dual
effects of nanopatterning depth and wt% of PCBM in the performance of
PCBM-PSCs with nanopatterned TiO2 layer and represent a
significant step towards the development of PSCs more efficiently by
examining and uncovering patterns of the available data through ML.