A Machine Learning Approach for Metal Oxide Based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells
CHEMPLUSCHEM, cilt.86, sa.5, ss.785-793, 2021 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 86 Sayı: 5
- Basım Tarihi: 2021
- Doi Numarası: 10.1002/cplu.202100132
- Dergi Adı: CHEMPLUSCHEM
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Chemical Abstracts Core, Chimica, EMBASE, MEDLINE
- Sayfa Sayıları: ss.785-793
- Süleyman Demirel Üniversitesi Adresli: Evet
Özet
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.