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

YILDIRIM M. O. , Gok E. C. , Hemasiri N. H. , EREN E. , Kazim S., ÖKSÜZ A. , ...More

CHEMPLUSCHEM, vol.86, no.5, pp.785-793, 2021 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 86 Issue: 5
  • Publication Date: 2021
  • Doi Number: 10.1002/cplu.202100132
  • Title of Journal : CHEMPLUSCHEM
  • Page Numbers: pp.785-793


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 R-2 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 R-2 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 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.