Yazar "Mallah, Shaimaa H." seçeneğine göre listele

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    Benzothiophene semiconductor polymer design by machine learning with low exciton binding energy: A vast chemical space generation for new structures
    (Elsevier Ltd, 2025) Mallah, Shaimaa H.; Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Güleryüz, Hasan; Mohyuddin, Ayesha; Kyhoiesh, Hussein A.K.
    The development of new organic semiconductors with low exciton binding energies (Eb) is crucial for improving the efficiency of organic photovoltaic (PV) devices. Here, we report the generation of a chemical space of benzothiophene (BDT)-based organic semiconductors with lowest Eb energies using machine learning (ML). Our study involves the design of over 500 organic semiconductor structures with low Eb energies and their synthetic accessibility scores. For this, we collect 1061 BDT based compounds from literature, calculated their Eb energies, and predicted them using ML with Random Forest (RF) regression, yielding the best results. Our analysis, using SHAP values, reveals that heavy atoms are the main factors in lowering Eb values. Furthermore, we tested new organic chromophore structures, which showed an efficient shift of their molecular charges. The UV–Vis spectra of these structures exhibits a redshift in the range of 358–667 nm, while their open-circuit voltage (Voc) and light-harvesting efficiency (LHE) ranges from 1.64 to 1.954 V and 52–91 %, respectively. Current study provides a valuable chemical space for the development of new organic semiconductors with improved efficiency. © 2025 Elsevier Ltd
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    Öğe
    Chemical modification-induced enhancements in quantum dot photovoltaics: a theoretical and molecular descriptive analysis
    (Springer, 2025) Hasan, Duha M.; Mallah, Shaimaa H.; Waheeb, Azal S.; Güleryüz, Cihat; Hassan, Abrar U.; Kyhoiesh, Hussein A. K.; Elnaggar, Ashraf Y.; Azab, Islam H. El; Mahmoud, Mohamed H. H.
    The study reports a molecular descriptive based design for carbon quantum dots (CQDT) to their photovoltaic (PV) performance. Taking C30H14 as an example, its new molecular systems as CQDT1-CQDT5 are optimized by Density Functional Theory (DFT). Their molecular descriptors are calculated with the help of a Python programming language package RDKit tool. Their Frontier Molecular Orbitals (FMOs) show a charge switching behavior, and UV–Vis analysis shows a redshift of their maximum absorption (λmax) values. Among their RDKit descriptors, their Bertz Complexity Topology (BertzCT) and molecular connectivity indices (χov) emerge as important for determining their Jsc. Pmax shows positive relation correlation. Further efficiency is analyzed through additional PV parameters while their electronic excitations are visualized using Multiwfn-based Transition Density Matrix (TDM) and electron–hole overlap analysis. This synergy of theoretical and molecular descriptor-related approaches could pave the way for the rational design of high-efficiency CQDTs as PV devices.