A 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 prepared, encompassing WO 3 ‐polyaniline (PANI), WO 3 ‐poly(N‐methylaniline) (PMANI), WO 3 ‐poly(2‐fluoroaniline) (PFANI), WO 3 ‐polythiophene (PTh), WO 3 ‐polyfuran (PFu) and WO 3 ‐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 WO 3 and its composites. For the evaluation of PSCs performance, a decision tree model that returns 0.9656 R 2 score is ideal for the WO 3 ‐PEDOT composite, while a random forest model was found to be suitable for WO 3 ‐PMANI, WO 3 ‐PFANI, and WO 3 ‐PFu with R 2 scores of 0.9976, 0.9968, and 0.9772 respectively. In the case of WO 3 , WO 3 ‐PANI, and WO 3 ‐PTh, a K‐Nearest Neighbors model was found suitable with R 2 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.