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.

Keywords: conjugated polymers; machine learning; perovskite solar cells; tungsten trioxide.