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    A DFT study on new photovoltaic dyes to investigate their NLO tuning at Near Infrared Region (NIR) as pull-push effect by end capped acceptors
    (2022) Hassan, Abrar U.; Sumrra, Sajjad H.; Nazar, Muhammad F.; Güleryüz, Cihat
    Throughout the opto-electronic devices industry, organic materials with considerable nonlinear optical (NLO) capabilities are being used. By employing 4,6-di(thiophen-2-yl)pyrimidine as a standard molecule, a series for new dyes (DMBMB1-DMBMB6) are created in the present paper by altering their functionalization with various electron acceptor (A) functional groups. The density functional fheory (DFT) and time dependent DFT (TD-DFT) based calculations have been performed to explore NLO responses by adjustment of different A units. The energy gap (Egap) of their highest occupied molecular orbitals (HOMOs) and lowest unoccupied molecular orbitals (LUMOs) was ranged between 0.22-2.43 eV which was also used to calculate their global chemical parameters (GRPs). All the new dyes were subjected to UV-Vis studies revealing their frequencies being red shifted from starting dye (DMBMB). The theoretical investigations like frontier molecular orbital (FMO) and natural bond orbital (NBO) analysis was used to investigate their intramolecular charge transfer (ICT). The dye DMBMB6 had the greatest linear polarizability, first hyperpolarizability (αtotal), and second order hyperpolarizability (βtotal) for all the developed dyes. In conclusion, due of their low ICT, all the dyes showed potential NLO features. Scientific researchers would be able to harness these NLO features to discover NLO materials for current and future uses.
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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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    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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    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.
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    Excited state dependent fast switching NLO behavior investigation of sp2 hybridized donor crystal as D-?-A push–pull switches
    (Elsevier B.V., 2024) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Nkungli, Nyiang K.; Muhsan, Muhammad S.; Alshehri, Saad M.
    This research focused on the investigating electronic and optical properties of designed chromophores (TPTP1-TPTP5) to involve their comprehensive analysis, including geometry optimization, UV–Vis spectroscopy, analysis of the transition density matrix (TDM), and exploration of their nonlinear optical (NLO) responses. The chromophore TPTP1 and TPTP2 exhibit significant transitions, making them suitable for optical switching applications. The chromophore TPTP5 stood out with high values for linear polarizability (<α0>, 8.53 × 10-24 esu), first order polarizability β0 (3.86 × 10-24 esu), and second order hyperpolarizability (γ0, 6.41 × 10-24 esu), making it notable for its nonlinear optical response. A positive correlation was observed between their vertical ionization potential (VIP) and the γ0 related NLO response, indicating that higher VIP values correspond to stronger γ0 responses. Their UV–Vis spectroscopy was employed to examine the absorption properties of the chromophores, revealing the wavelengths (λmax) at which they absorbed light and their potential for light harvesting applications. The analysis of the TDM allowed for a deeper understanding of the redistribution of electron density during electronic transitions within the chromophores. This analysis provided valuable insights into the characteristics and nature of their excited states. Additionally, the research investigated the NLO responses of the chromophores, particularly focusing on their third harmonic generation (THG) properties. These NLO properties are crucial for potential applications in optical switches, frequency conversion, and optical signal processing. Overall, the findings from this research contribute to a comprehensive understanding of the electronic and optical properties of the designed chromophores. The obtained results open up new possibilities for their utilization in various technological fields, including light harvesting, photonics, and nonlinear optics.
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    Exploration of deep UV-NLO responses of non-conjugated crystal systems by harnessing aprotic solvents: DFT vs experimental
    (2024) Güleryüz, Cihat; Mohyuddin, Ayesha; Hassan, Abrar U.
    This study introduces a newly found form of a non-conjugated crystal called 1,3,5-triazine-2,4,6-triamine polymorph (TT), TT ), which demonstrates a well-rounded UV nonlinear optical (NLO) behavior. By employing solvent-assisted Tauc bandgap adjustment, the crystal's structure was altered, leading to improved optical characteristics. The material's monoclinic polymorph displays adjustable bandgaps that are impacted by the choice of solvent during the crystallization process. When evaluated against a chemical potential of-2.61 eV, its characteristics were forecasted. Charge displacement across the aromatic ring is observed in the solvent stage, while molecular electrostatic potential (MEP) surfaces highlighted the nitrogen atoms as nucleophilic. The comparative examination of its energy gaps (E gaps ) offers significant perspectives on their prospective uses. Understanding these energy differences is crucial for choosing materials for specific applications like electronic devices, sensors, or catalysis. Moreover, comprehending charge distributions and nucleophilic tendencies can influence the creation of new materials with customized attributes for diverse uses in fields such as chemistry, materials science, and beyond.
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    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.
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    Exploring the structural basis of crystals that affect nonlinear optical responses: An experimental and machine learning quest
    (Elsevier B.V., 2025) Hassan, Abrar U.; Güleryüz, Cihat; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
    Machine learning can enable a computational framework to learn from data, thereby enhancing decision-making for targeted properties. Based on the significance of nonconjugated crystals as effective switches, an ML based approach has been applied to evaluate driving forces behind their polarizability/hyperpolarizability related hyper-Rayleigh Scattering (βHRS). For this, a dataset of relevant 1,3,5-triazine-2,4,6-triamine related structures in collected from peer reviewed literature to design its molecular descriptors. The designed dataset is trained on different regression models along with their cross-validation techniques include K-Fold and Leave One Group Out. It shows that Random Forest Regression can predict their polarizabilities with a fair accuracy (R2 = 0.83). Additionally, it shows its energy gaps (Egaps) ranging from 4.62 to 4.89 eV, with the smallest gap observed in ethanol. Understanding both these theoretical and experimental calculations can significantly help in selecting materials for targeted purposes, including sensors, electronic devices, and catalysis. Furthermore, insights into nucleophilic tendencies and charge distributions aids in designing new materials with tailored properties, expanding their use in various applications across chemistry, materials science, and other fields. The ML techniques prove its effectiveness to predict polarizabilities in response to its computational realm due to feature design, regression models with their cross-validations.
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    A machine learning and data-oriented quest to screen the degree of long-range order/disorder in polymeric materials
    (Elsevier, 2025) Guleryuz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Mahmoud, Mohamed. H. H.
    In the realm of polymeric materials, the delicate balance between long-range order and disorder dictates crystal properties, influencing their performance in various applications. To unravel this enigma, we embarked on a machine learning (ML) and data-driven quest, compiling 2500 data points from literature. By harnessing the power of Support Vector Machines (SVM) and Radial Basis Functions (RBF), we trained our model to decipher the intricate relationships between molecular descriptors and crystal properties. Introducing a novel pass/fail system, we screened polymers based on their calculated descriptors, revealing that combining multiple descriptors significantly enhances model performance. Identifying 1200 polymers that failed to meet crystallization requirements provides valuable insights for designing materials with tailored structural features. This groundbreaking study pioneers a data-oriented approach to understanding polymeric materials, paving the way for the creation of novel crystals with optimized properties. By uncovering the hidden patterns of order and disorder, we unlock the secrets of polymeric materials, revolutionizing their applications in various fields.
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    Novel pull–push organic switches with D–?–A structural designs: computational design of star shape organic materials
    (Springer, 2022) Hassan, Abrar U.; Mohyuddin, Ayesha; Güleryüz, Cihat; Nadeem, Sohail; Nkungli, Nyiang K.; Hassan, Sadaf U.; Javed, Mohsin
    The structural alteration with π-linkers was used to design a donor–acceptor type series of 2,2′-(pyrimidine-4,6-diyl)bis(2,3-dihydro-1,3-benzothiazole) (PB)-based chromophores (AH1–AH7) to exploit the adjustments in their optical characteristics. To investigate the electronic geometries, absorption wavelengths, charge transfer processes, and the efect of structural alterations on nonlinear optical (NLO) characteristics, density functional theory (DFT) simulations have been used. During the UV–visible study, several long-range and range separated functionals like B3LYP, CAM-B3LYP, B97XD, and APFD with the 6-311G+(d,p) basis set were used to select the efcient level at DFT. As a response, UV–vis data indicated an intriguing consistency at the B3LYP level across experimental and TD-DFT-based values of PB. All the designed molecules had a smaller energy band gap (0.84–3.67 eV) and wide absorption spectra inside the visible region. Natural bond orbital (NBO) results indicated a signifcant push–pull operation, with donors and π-conjugates exhibiting positive values and most acceptors exhibiting the minimum values. Electronic transformations between electron donors to acceptor moiety, Trifuoromethyl (TFM) via π-conjugated linkers were shown to have a superior linear ˂α>and nonlinear (βtotal) NLO values of 306–474 and 40–230 Debye-Angstrom−1 respectively. When chromophores with one phenyl π-linker were compared to those with the two π-linkers, the chromophores with the higher π-linker showed increased hyperpolarizability. The highest second-order hyperpolarizability (β) was found to be 230.11 DebyeAngstrom−1 which was about fve times higher than urea (standard). This research has shown that by manipulating the kind of π-spacers, novel metal-free NLO compounds may be created, which might be used for high-tech NLO purposes.
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    On the quest for solar energy harvesters and nonlinear optics: a DFT exploration of A-D-D-A framework with varying sp2 hybridization
    (Springer, 2025) Güleryüz, Cihat; Rehman, Muhammad M. U.; Hassan, Abrar U.; Abass, Zainab A.; Mohyuddin, Ayesha; Zafar, Muddassar; Alotaibi, Mohammed T.
    In response to address the constraints of fullerene analogues, scientists are constantly working on developing low-cost fullerene-free functionalization for nanoscale organic photovoltaics. During the present study, the computational design and analysis of 14 new non-fullerene dyes (IDIC-O-1 to IDIC-O-14) centered on indacenodithiophene (IDIC) core are proposed with sp2-hybridized nitrogen at varying positions. Regarding their UV–visible assessment, several long-range and range-separated functionals like B3LYP, CAM-B3LYP, ωB97XD, and APFD using the 6-311G + (d,p) basis set have been employed to identify their optimal level of density functional theory (DFT) with an impressive correlation at the CAM-B3LYP level. Their global hardness (η) and global electrophilicity (ω) natures show their persistent nature. The energy gaps (Egaps) are lesser than IDIC and IDIC-O to imply an easier electronic transition. When contrasted to the IDIC-O, the findings indicate that its broad absorption spectrum had a redshift. The efficient HOMO → LUMO-based CT was investigated, and an open-circuit voltage (Voc) study is done on HOMOIDIC → LUMOAcceptor. All dyes have their Voc values lower than reference (IDIC-O) except IDIC-O-11 with a positive value. These lower reorganization energies (RE) for holes and electrons indicate a greater charge transfer (CT). When contrasted to the IDIC-O, the newly designed dyes have better characteristics for solar cell performance.
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    Theoretical calculations of nonlinear optical responses for interpreting nonconjugated molecular systems to affect non-optimal properties
    (2024) Güleryüz, Cihat; Sumrra, Sajjad H.; Mohyuddin, Ayesha; Hassan, Abrar U.; Dahshan, Alaa
    Understanding the intricacies of polymorphic origins in nonconjugated crystal systems is crucial for optimizing their properties. This study focuses on the crystal growth, characterization, and nonlinear optical (NLO) responses of a system analyzed using single crystal X-ray analysis, revealing a monoclinic geometry. Hirschfeld surface analysis emphasized the significance of intermolecular interactions in driving polymorph development, shedding light on the structural nuances influencing the material's properties. Through the evaluation of density functional theory parameters, the research found that the NLO responses of the system were as efficient as those of widely recognized materials like urea and KDP. Moreover, the stability of the system was confirmed through (NBO) analysis, showcasing its potential for practical applications. By manipulating the polymorphic crystal forms, researchers can potentially unveil new crystalline materials with tailored properties suitable for applications in optical and optoelectronic devices. This work underscores the importance of exploring novel crystal engineering strategies to harness the full potential of materials in the realm of advanced technologies.
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    Theoretical probing of 3D nano metallic clusters as next generation non-linear optical materials
    (Elsevier, 2022) Hassan, Abrar U.; Sumrra, Sajjad H.; Nkungli, Nyiang K.; Güleryüz, Cihat
    The excess electron containing compounds have exceptional initial hyper polarizabilities (σ), making them promising nominees for next generation non-linear optical materials. We investigated the geometric, thermodynamic, electrical, and nonlinear optical aspects of a highly strained, theoretically designed metallic cluster (MC), (Fe3Se2(CO)8, in this paper. The designed MC was thermally stable. Estimated ionization energy was used to characterize electrical stability nature (IE). Moreover, the significantly reduced EH–L values reflected the MC with its outstanding characteristics. The maximum absorption (λmax) for computed absorption of electronic transitions was estimated between 327 nm and 340 nm and HOMO → LUMO transitions were found to be the dominant electronic transition band in the UV–Vis spectral region. When comparing to the excited spectrum, the stimulated spectrum appeared to be substantially blue-shifted, with a wide band between 400 and 700 nm. It had the hyperpolarizability values of up to 4.3 × 104 au, resulting in a significant drop in excited state and higher hyperpolarizability values. Using the traditional two-level model, the resulting first hyperpolarizability was also explained. In this MC, the projections of hyperpolarizability on dipole moment coincided with overall hyperpolarizability, showing unidirectional charge transfer with polarizability at four basis sets (B3LYP, CAM-B3LYP, WB97XD and PBEPBE). The static second hyperpolarizability (β) value of the examined MC was higher. The recent discovery, we feel, can provide inspiration for further research into alternative excess electron first row transition MC for NLO applications.