A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells
Murat Onur Yildirim, Elif Ceren Gök, Naveen Harindu Hemasiri, Esin Eren, Samrana Kazim, Ayşegül Uygun Öksüz, Shahzada Ahmad
Abstract
Abstract 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.