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

Abstract

A library of metal oxide-conjugated polymer composites was prepared, encompassing 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) which were used 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. For the evaluation of PSCs performance, a decision tree model that returns 0.9656 R2 score is ideal for the WO3 -PEDOT composite, while a random forest model was found to be suitable for WO3 -PMANI, WO3 -PFANI, and WO3 -PFu with R2 scores of 0.9976, 0.9968, and 0.9772 respectively. In the case of WO3 , WO3 -PANI, and WO3 -PTh, a K-Nearest Neighbors model was found suitable with R2 scores of 0.9975, 0.9916, and 0.9969 respectively. Machine learning can be a pioneering prediction model for the PSCs performance and its validation.