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 withJ_sc , V_oc , 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 J_sc , 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 J_sc . Furthermore, the incorporation of PCBM into the perovskite layer enhanced the electron transport property of PSCs, yielding to the increment in V_oc 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.