Yazar "Noreen, Sadaf" seçeneğine göre listele

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    A fast and efficient machine learning assisted prediction of urea and its derivatives to screen crystal propensity with experimental validation
    (Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Noreen, Sadaf; Elnaggar, Ashraf Y.
    Predicting crystal propensity is crucial yet challenging in various industries where it significantly influences product stability, performance, and efficacy. Predicting a crystal propensity identifies their optimal chemical structures for desired properties including solubility, bioavailability, shelf-life stability etc. Herein, A machine learning (ML) assisted analysis is performed to predict their crystal propensity by collecting a dataset of 6000 non-crystalline and over 200 crystalline urea and its derivatives. The data is trained by employing a Support Vector Machine (SVM) with its Radial Basis Function (RBF) and linear kernels along with Random Forest regression analysis. The trained data is compared with four other ML models, including Linear Regression, Gradient Boosting, Random Forest and Decision Tree Regressions to predict their crystal propensity. It yields an accuracy of 79 % for identifying their non-crystalline compounds and 59 % in predicting crystallization failure. Their dimensionality reduction via t-SNE reveals their distinct clustering patterns to underscore their complex interplay between molecular structure and crystal propensity. Their experimental validation also corroborates the current findings to demonstrate their efficacy to streamline their crystal engineering for pharmaceutical formulation-based workflows. Notably, the number of rotatable bonds and molecular connectivity index (χov) emerges as pivotal descriptors for enabling their accurate classification with minimal input features. This study elucidates its quantitative structure-crystallinity relationship to provide a valuable tool for crystal design and optimization.
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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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    DFT-guided structural modeling of end-group acceptors at Y123 core for sensitizers as high-performance organic solar dyes and NLO responses
    (2023) Hassan, Abrar U.; Sumrra, Sajjad H.; Zafar, Muddassar; Mohyuddin, Ayesha; Noreen, Sadaf; Güleryüz, Cihat
    Context: The organic solar cells (OSCs) are being developed with the goal of improving their photovoltaic capabilities. Here, utilizing computational methods, six new nonfullerene acceptors (NFA) comprising dyes (A1-A6) have been created by end-group alterations of the Y123 framework as a standard (R). Methods: The DFT-based investigations at B3LYP/6-31G + (d,p) level were applied to evaluate their properties. The planar geometries associated with these structures, which lead to improved conjugation, were validated by the estimation of molecular geometries. Dyes A1-A6 have shorter Egap than R, according to a frontier molecular orbital (FMO) investigation, which encourages charge transfer in them. The dyes with their maximum absorption range were shown by optical properties to be 692-711 nm, which is significantly better than R with its 684 nm range. Their electrostatic and Mulliken charge patterns provided additional evidence of the significant separation of charges within these structures. All the dyes A1-A6 had improved light harvesting efficiency (LHE) values as compared to Y123, highlighting their improved capacity to generate charge carriers by light absorption. With the exception of dye A4, all newly developed dyes might have a superior rate of charge carrier mobility than R, according to reorganization energies λre. Dyes A3 and A4 had the greatest open-circuit voltage (Voc). Dye A3 exhibited improvement in all of its examined properties, making it a promising choice in DSSC applications.
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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.