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    A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications
    (Elsevier B.V., 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Waheeb, Azal S.; Awad, Masar A.; Jalfan, Ayad R.; Noreen, Sadaf; Kyhoiesh, Hussein A.K.; El Azab, Islam H.
    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.
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    Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials : A DFT and ML analysis
    (Elsevier Ltd, 2024) Kadhum, Afaf M.; Waheeb, Azal S.; Awad, Masar A.; Hassan, Abrar U.; Sumrra, Sajjad H.; Güleryüz, Cihat; Mohyuddin, Ayesha; Noreen, Sadaf; Kyhoiesh, Hussein A.K.; Alotaibi, Mohammed T.
    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.
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    Öğe
    Molecular engineering on tyrian puprle natural dye as TiO2 based fined tuned photovoltaic dye material: DFT molecular analysis
    (2024) Güleryüz, Cihat; Hasan, Duha M.; Awad, Masar A.; Waheeb, Azal S.; Hassan, Abarar U.; Mohyuddin, Ayesha; Kyhoiesh, Hussein A. K.; Alotaibi, Mohammed T.
    In this research, molecular modification is employed to see the enhancement in the efficiency of Tyrian Purple (TP), a natural dye, for organic photovoltaic materials. By using Density Functional Theory (DFT) based molecular modeling, seven new structures are designed with pi spacer to extend electron donor moieties. Teheir Frontier Molecular Orbital (FMO) analysis demonstartes their charges with a similar pattern of distributions over their Highest Occupied and Lowed Unocuupied Molecular Orbitals (HOMO/lUMO). This analysls also show their energy gaps (Egaps) to range around 2.97-3.02 eV. Their maximum absorption wavelength (λmax) demosntartes 486-490 nm range to indicate their tendency of absorbing light efficiently. Their Transition Density Matrix (TDM) analysis also reveals their facile electronic transitions without a significant charges over spacers. From calculating their photovoltaic paramters, their Light Harvesting Efficiency (LHE) reaches to 72.4-95.5 %. Also their Open Circuit Voltage (Voc) varies across 1.16-1.34 V. It is found that dyes actively adsorb onto TiO2 clusters to demonstrate their promise for tuning their Conduction Band (CB). This research is an effort for to evaluate the structural correlations to the develop photovoltaic materials through molecular-level design and optimization.