A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications
[ X ]
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We present a synergistic approach to combine Machine Learning (ML), Density Functional Theory (DFT), and molecular descriptor analysis for designing high-performance benzodithiophene (BDT) based chromophores. A dataset of 366 BDT incorporated moieties is compiled from literature while their molecular descriptors are designed by using Python programming language. Linear and Random Forest Regression models produces best results to predict their exciton binding energy (Eb) with their R-Squared (R2) value 0.87 and 0.94 respectively. Their DFT calculations provides additional features, including molecular charges. Their ML models also reveals that their Eb values are a crucial predictor for their photovoltaic (PV) performance as its lower value could facilitate efficient charge carrier separation. For this, their hydrogen bond acceptors (HBA) and topological polar surface area (TPSA) emerges as key descriptors during their regression analysis. Their DFT validation shows negligible differences in their molecular charges to suggest their electron donor/acceptor moieties can significantly impact their chromophore nature. The current research work is helpful for efficiently screening the suitability of organic chromophores for their PV applications through advanced computational tools.
Açıklama
Anahtar Kelimeler
Benzodithiophene, DFT calculations, Exciton Binding Energy, ML, Photovoltaic Chromophores
Kaynak
Journal of Photochemistry and Photobiology A: Chemistry
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
460
Sayı
Künye
Güleryüz, C., Sumrra, S. H., Hassan, A. U., Mohyuddin, A., Waheeb, A. S., Awad, M. A., Jalfan, A. R., Noreen, S., Kyhoiesh, H. A. K., El Azab, I. H. (2025). A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications. Journal of Photochemistry and Photobiology A: Chemistry, 460. 10.1016/j.jphotochem.2024.116157
