Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials : A DFT and ML analysis
[ X ]
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We present a new study on the design, discovery and space generation of carbon selenide based photovoltaic (PV) materials. By extending acceptors and leveraging density functional theory (DFT) and machine learning (ML) analysis, we discover new QDs with remarkable PV properties. We employ various ML models, to correlate the exciton binding energy (Eb) of 938 relevant compounds from literature with their molecular descriptors of structural features that influence their performance. Our study demonstrates the potential of ML approaches in streamlining the design and discovery of high-efficiency PV materials. Also the RDKit computed molecular descriptors correlates with PV parameters revealed maximum absorption (λmax) ranges of 509–531 nm, light harvesting efficiency (LHE) above 92 %, Open Circuit Voltage (Voc) of 0.22–0.45 V, and short Circuit (Jsc) currents of 37.92–42.75 mA/cm2. Their Predicted Power Conversion Efficiencies (PCE) using the Scharber method reaches upto 09–13 %. This study can pave the way for molecular descriptor-based design of new PV materials, promising a paradigm shift in the development of high-efficiency solar energy conversion technologies.
Açıklama
Anahtar Kelimeler
Machine Learning, Power Conversion Efficiency, Quantum Dots, RDKit, Scharber method
Kaynak
Solar Energy
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
284
Sayı
Künye
Kadhum, A. M., Waheeb, A. S., Awad, M. A., Hassan, A. U., Sumrra, S. H., Güleryüz, C., Mohyuddin, A., Noreen, S., Kyhoiesh, H. A. K., Alotaibi, M. T. (2024). Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials: A DFT and ML analysis. Solar Energy, 284. 10.1016/j.solener.2024.113068
