Machine Learning Approach for Metal Oxide based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells

A library of metal oxide-conjugated polymer composites was synthesized, encompassing of WO₃-polyaniline (PANI), WO₃-poly(N-methylaniline) (PMANI), WO₃-poly(2-fluoroaniline) (PFANI), WO₃-polythiophene (PTh), WO₃-polyfuran (PFu) and WO₃-poly(3,4-ethylenedioxythiophene) (PEDOT). These composites were probed as hole selective layers for perovskite solar cells (PSCs) fabrication. We adopted machine learning approaches to predict and compare PSCs performances with the developed WO₃ and its composites. The experimental and theoretical results are coherent, when the electro-optical properties of PSC were computed. Notably, for the evaluation of PSCs performance, decision tree model is the ideal for WO₃-PEDOT composite, while random forest model was found to be suitable for WO₃-PMANI, WO₃-PFANI, WO₃-PFu. While in the case for WO₃, WO₃-PANI and WO₃-PTh, K Nearest Neighbors model was appropriate. Machine learning models can be a pioneering prediction models for the PSCs performance and its validation.