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Öğe 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.Öğe 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 LtdÖğe 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.Öğe Exploring structural basis of photovoltaic dye materials to tune power conversion efficiencies: A DFT and ML analysis of Violanthrone(Elsevier Ltd, 2025) Sumrra, Sajjad H.; Güleryüz, Cihat; Hassan, Abrar U.; Abass, Zainab A.; Hanoon, Talib M.; Mohyuddin, Ayesha; Kyhoiesh, Hussein A.K.; Alotaibi, Mohammed T.This study employs a systematic approach to modify Violanthrone (V) structures and analyze their impact on photovoltaic (PV) properties. We use cheminformatics based Python library based RDKit tool to calculate their structural descriptors for to correlate them with their PV parameters. Our analysis reveals a positive correlation for their Open-Circuit Voltage (Voc) and Fill Factor (FF) for indicating that their higher voltage output is associated for their efficient charge carrier mobilities. We also predict their Power Conversion Efficiency (PCE) by drawing their their Scharber diagram which achieves their promising efficiency of up to 15 %. To further enhance the reliability our work, we conduct an extensive literature survey of such organic materials to predict their PCEs by their Machine Learning (ML) after utilizing various ML models. Among five tested ML models, it identifies the Random Forecast (RF) model and Gradient Boosting (GB) models as as the optimal one (R-squared value: 0.82). Their feature importance reveals that their FF is the most significant feature to impact their PCEs (importance value: 10.9). Furthermore, we observe a negative correlation between orbital interaction strength (E(2)) values and orbital energy differences E(j)-E(i) which indicates that their stronger orbital interactions are associated with their smaller energy differences. Our study provides valuable insights for their structural basis to PV material designs for enabling their design for efficient materials in energy conversion.
