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The comorbidities of hidradenitis suppurativa
(J.B. Lippincott, 2025) Aşkın, Özge; Ferhatoğlu, Özge Altan; Özkoca, Defne; Küçükoğlu Cesur, Seher; Tüzün, Yalçın
Hidradenitis suppurativa is a chronic inflammatory disease that dramatically decreases the quality of life of afflicted patients. A number of factors may coexist with hidradenitis suppurativa, including stigmatization, social isolation, tobacco use, alcohol abuse, suicidal ideation, depression, other psychiatric disorders, and medical comorbidities such as obesity, diabetes mellitus, hypertension, dyslipidemia, metabolic syndrome, coronary artery disease, and polycystic ovarian syndrome. These comorbidities should be kept in mind while planning the treatment. A rare but important long-term complication of hidradenitis suppurativa is squamous cell cancer; men with perianal, gluteal, or perineal lesions are at increased risk, and multiple biopsies should be taken in case of any suspicious lesions.
Investigation of APOE2 rs7412 and APOA2 rs5082, APOA5 rs662799 and MTHFR rs1801133 Polymorphisms in Diabetic Obese and Non-Obese Diabetic Groups in Turkey
(Pleiades Publishing, 2025) Özyavuz, M. K.; Durak, S.; Çelik, F.; Aksoyer Sezgin, S.B.; Gürol, A.O.; Doğan, Z.; Zeybek, U.
Abstract: Obesity caused by an abnormal increase in adiposyte, is linked to type 2 diabetes. Diabetes is a metabolic-disease that is caused by insulin-deficiency. Type 2 diabetes is a serious implication of obesity and genetic polymorphisms. Apolipoprotein E2 (APOE2)(rs7412), Apolipoprotein A2 (APOA2) (rs5082), Apolipoprotein A5 (APOA5) (rs662799), Methylenetetrahydrofolate reductase (MTHFR) (rs1801133) polymorphisms which were determined to play role in the development of obesity and diabetes, were evaluated with the RT-qPZR in the study. We included 99 diabetic obese and 99 diabetic non-obese people. We investigated the effect of obesity on variants of all gene polymorphisms in diabetic patients. As a result, APOA2, APOE2 polymorphisms were significant, APOA5, MTHFR polymorphisms were not significant in genotype/allele frequency between groups. APOA2 CC-homozygous carriers had high low-density-lipoprotein-c, glucose, body-mass-index in diabetic-obese patients. APOE2 C-allele carriers had significantly high-Triglyceride and low high-density-lipoprotein versus TT-genotype in non-obese diabetic patients. The present study was first in the Turkey population and evaluates the polymorphisms of genes indicated in diabetic patients.
Comprehensive analysis and machine learning-based solutions for drift behavior in ambient Atomic Force Microscope conditions
(Elsevier Ltd, 2025) Deveci, Derya Gemici; Barandır, T. Karakoyun; Ünverdi, Ö.; Çelebi C.; Temur, L.Ö.; Atilla, D.Ç.
This study outlines the effectiveness of combining numerical methods, Computer Vision (CV) and Machine Learning (ML) approaches to analyze and predict drift behavior in high-resolution Atomic Force Microscope (AFM) scanning procedures. Using Long Short-Term Memory (LSTM) models for time series analysis and the Light Gradient Boosting Machine (LightGBM) algorithm for predictive modeling, significant progress was achieved in understanding the dynamic and variable nature of drift and mitigating its impact on scanning. The models demonstrated a robust predictive capability, achieving approximately 94% accuracy in drift predictions. The study emphasizes the nonstationary characteristics of drift and demonstrates how the selection of features directly related to the target variable enhances the efficiency of the model and enables adaptive real-time correction. These findings confirm the predictive strength of the models and highlight the potential for integrating ML predictions with real-time feedback mechanisms to improve the resolution and stability of AFM imaging in both scientific and industrial applications.
Bandgap tuned indaceno based polymer design by machine learning for chemical space by descriptor designing and data guidelines
(Elsevier Ltd, 2025) Sumrra, Sajjad H.; Güleryüz, Cihat; Hassan, Abrar U.; Afzaal, Maria; Siddiqa, Ayesha; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
This research introduces an innovative approach for developing bandgap-tuned polymers by machine learning assisted modeling of indaceno polymer chemical space. Using a dataset of indaceno donor moieties, the descriptors that encode fundamental molecular features are designed and trained to predict their bandgaps. After three modeling rounds of breaking retrosynthesis in Python, 1000 new polymers with their bandgaps are designed. The study shows that descriptors like valence electrons, Labute ASA, and Morgan Density has a significant impact on model performance to highlight their key role. Additionally, the synthetic accessibility scores of newly designed polymers reach a maximum of 17 for RDkit based descriptors to indicate their ease of synthesis and promising practical applicability. This work not only deepens the understanding of indaceno polymers but also lays the groundwork indaceno based polymer design of through data-driven approaches.
A comparative experimental study on the collection and analysis of DNA samples from under-fingernail materials
(Taylor & Francis, 2025) Yüksel, Elif; Karadayı, Şükriye; Özbek, Tülin; Karadayı, Beytullah
In cases of murder and rape where there is physical contact between the perpetrator and the victim, analysis of the victim's fingernail material is quite valuable. Although it is possible that the foreign DNA detected in the fingernail material does not belong to the perpetrator of the incident, if it does belong to the perpetrator of the incident, it may provide useful findings for solving the incident. Fingernail material collected after the incident often contains mixed DNA. The efficiency of sample collection procedures is of particular importance, as this process may pose some problems in the interpretation of autosomal short tandem repeat analyses used for the identification of the individual or individuals. The aim of this study is to compare three different fingernail material collection procedures (thick-tipped swabbing, thin-tipped swabbing and nail clipping) to determine the most efficient sample collection procedure and to contribute to routine investigations to identify the assailant in forensic cases. In our study, under-fingernail materials was collected from 12 volunteer couples by three different methods. To help compare the efficiency of the three different methods, the profiles obtained were classified based on the number of female and male alleles detected. In the obtained short tandem repeat profiles, while nail clipping yielded 58.3% (n= 7) "High level DNA mixture" as a profile containing 12 or >12 female alleles, 75% (n= 9) of the samples taken with cotton-toothpick swabs (thin-tipped) yielded "Full male profile". In conclusion, our study shows that cotton-toothpick swabs (thin-tipped) are the most efficient method for determining the male DNA profile among three different fingernail material collection procedures. We suggest that using thin-tipped swabs produced in a specific standard, instead of the standard swabs commonly used in routine crime investigations, to identify perpetrators from fingernail material may improve the efficiency of processing nail material and evaluating evidence.
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.
Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study
(2025) Tanyıldızı-Kökkülünk, Handan
Objectives: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols. Methods: A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances. Results: The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses. Conclusions: The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.
Enhancing Radiotherapy Tolerance With Papaya Seed-Derived Nanoemulsions
(Malden, 2025) Siddiqui, Muhammad Tariq; Ölçeroğlu, Bilge; Gümüş, Zinar Pınar; Şenışık, Ahmet Murat; Barlas, Fırat Barış
Flavonoid-rich plant materials have gained attention for their potential to reduce radiotherapy side effects. Carica papaya (CP) seeds, known for high flavonoid content, hold promise for therapeutic applications. This study explored the extraction and evaluation of two oils-sunflower oil-based papaya oil (SPO) and pure papaya oil (PPO)-and their nano emulsions (SPOE and PPOE), derived from CP seeds, for radioprotective effects. Chemical analysis using QTOF-MS revealed antioxidants and phytochemicals in the oils and emulsions. Size analysis and zeta potential measurements using dynamic light scattering (DLS) showed particle sizes of 140 ± 26.06 nm for PPOE and 293.7 ± 49.42 nm for SPOE. Post-radiation, both SPOE and PPOE significantly enhanced cell viability, with values of 72.24 ± 3.92 (p ≤ 0.001) and 75.85 ± 2.62 (p ≤ 0.001), respectively. These nanoemulsions show potential as topical agents for reducing radiation-induced tissue damage in radiotherapy. Despite the promising in vitro findings, further in vivo studies are needed to confirm the clinical relevance of these nanoemulsions. Additionally, their incorporation into sunscreen formulations could provide further protection against radiation-induced skin damage, broadening their potential applications.
Probabilistic Risk Framework for Nuclear- and Fossil-Powered Vessels: Analyzing Casualty Event Severity and Sub-Causes
(MDPI, 2025) Tanyıldızı-Kökkülünk, Handan; Kökkülünk, Görkem; Settles, John
Maritime activities pose significant safety risks, particularly with the growing presence of nuclear-powered vessels (NPVs) alongside traditional fossil-powered vessels (FPVs). This study employs a probabilistic risk assessment (PRA) approach to evaluate and compare accident hazards involving NPVs and FPVs. By analyzing historical data from 1960 to 2024, this study identifies risk patterns, accident frequency (probability), and severity levels. The methodology focuses on incidents such as marine incidents, marine casualties, and very serious cases with sub-causes. Key findings reveal that Russia exhibits the highest risk for very serious incidents involving both NPVs and FPVs, with a significant 100% risk for NPVs. China has the highest FPV risk, while France and the USA show above-average risks, particularly for marine casualties and very serious incidents. Moreover, collision is the most significant global risk, with a 26% risk for NPVs and 34% for FPVs, followed by fire hazards, which also pose a major concern, with a 17% risk for NPVs and 16% for FPVs, highlighting the need for enhanced safety and fire-prevention measures. In conclusion, comparative analysis highlights the need for enhanced stability improvements, fire prevention, and maintenance practices, particularly in the UK, France, Russia, and China. This study underscores the importance of targeted safety measures to mitigate risks, improve ship design, and promote safer maritime operations for both nuclear- and fossil-fueled vessels.
A machine learning assisted designing and chemical space generation of benzophenone based organic semiconductors with low lying LUMO energies
(Elsevier Ltd, 2025) Güleryüz, Cihat; Hassan, Abrar U.; Güleryüz, Hasan; Kyhoiesh, Hussein A.K.; Mahmoud, Mohamed H.H.
Current study presents a machine learning (ML) approach to design benzophenone-based organic chromophore with their lowest possible LUMO energy (ELUMO). A dataset of their 1142 donors is collected from literature and their molecular descriptors are designed by using RDKit. Among various models, the Random Forest regression model produces accurate results to predict their ELUMO values. Based on these predictions, their 5000 new donors are designed with their Synthetic Accessibility Likelihood Index (SALI) scores. Their SHAP value analysis reveals that their electro topological state indices are the most critical descriptors to lowering ELUMOs. The top- performing donor are further extended with acceptors and their photovoltaic (PV) properties by density functional theory (DFT). Their results show their maximum open-circuit voltage (Voc) of 2.30 V, a short-circuit current (Jsc) of 47.19 mA/cm2, and a light-harvesting efficiency (LHE) of 93 %. This study demonstrates the potential of ML assisted design to design new organic chromophores.
A graph neural network assisted reverse polymers engineering to design low bandgap benzothiophene polymers for light harvesting applications
(Elsevier Ltd, 2025) Hassan, Abrar U.; Güleryüz, Cihat; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
In this study, we present a novel approach to reverse polymer engineering utilizing a Graph Neural Network (GNN) framework to design low bandgap benzothiophene (BT) polymers for light harvesting applications. We have curated an extensive dataset comprising 57,556 structure-property pairs of BT-based compounds, leveraging expert knowledge to enhance the quality and relevance of the data. Our Transformer-Assisted Oriented pretrained model for on-demand polymer generation (TAO) demonstrates exceptional performance, achieving a chemical validity rate of 99.27 % in top-1 generation mode across a test set of 6000 generated polymers, marking the highest success rate reported among polymer generative models to date. Throughout the training process, the loss steadily decreased with each epoch, indicating that the model was learning effectively from the data. The model predictive accuracy is further validated by an impressive average R2 value of 0.96 for 15 defined properties, highlighting the TAO with its robust capabilities in polymer design. The newly designed polymers exhibit a bandgap range of 1.5–3.40 eV, making them promising candidates for light harvesting applications. Additionally, their highest Synthetic Accessibility Likelihood Index (SALI) scores reach up to 17 and also indicates that the majority of these polymers are amenable to synthesis. This work not only advances the field of polymer design but also provides a powerful tool for the targeted development of materials with specific electronic properties.
A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials
(Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Elnaggar, Ashraf Y.; Noreen, Sadaf
The selenium-based compounds are gaining significance for their surface-enhanced properties. In order to accelerate their discovery, a machine learning (ML) approach has been employed to predict their structural correlations. For this a dataset of 618 compounds is collected from literature and is trained by using Support Vector Machine (SVM) with its Linear Kernal. Among ten ML evaluated models, three top-performing models are selected to make predictions for their stability energy. A Convex Hull Distribution (CHD) is constructed to elucidate the relationship for their stability and structural correlations. The main finding of this study reveals its strong correlation between stability and its related structural descriptors, particularly Bertz Branching Index" corrected for the number of Terminal atoms (BertzCT), Partial Equalization of Orbital Electronegativities-Van der Waals Surface Area with 14 bins (PEOE_VSA14), and First-Order Connectivity Index (chi 1). The analysis demon strates that the current ML models can effectively predict the stability of such materials to enable their rapid screening. Their calculations can provide a framework to understand their complex relationships between their material properties, structure, and stability.
Recent advances of structure, function, and engineering of carboxylesterases for the pharmaceutical industry: A minireview
(IPC Science and Technology Press, 2025) Sürmeli, Yusuf; Vardar-Yel, Nurcan; Tütüncü, Havva Esra
Carboxylesterases have a wide range of applications due to their catalytic efficiency, robust structure, and broad substrate specificity. These enzymes, which can hydrolyze carboxylic acid esters, amides, and thioesters, stand out with their regio- and enantioselective properties. They play a crucial role in synthesizing pharmaceutical intermediates, including secondary and tertiary alcohols, α-hydroxy acids, and various bioactive compounds. However, in some cases, the enantioselectivity of carboxylesterases may be insufficient to achieve conversions with the purity required by the pharmaceutical industry. This review summarizes the crucial role of carboxylesterases, particularly in the pharmaceutical field, focusing on the classification, structure, and engineering approaches. After introducing the main families of carboxylesterases, the structural studies are presented to give a comprehensive insight into the active site architecture and related key determinants for enantioselectivity. The protein engineering studies to improve the enantioselectivity of carboxylesterases are discussed along with solvent engineering and immobilization applications.
Dual-Functioning Metal-Organic Frameworks: Methotrexate-Loaded Gadolinium MOFs as Drug Carriers and Radiosensitizers
(2025) Karaca, Burcu; Sakarya, Deniz; Siyah, Pınar; Şenışık, Ahmet Murat; Kaptan, Yasemin; Çavuşoğlu, Ferda C.; Mansuroğlu, Demet S.; Öztürk, Sadullah; Bayazıt, Şahika S.; Barlas, Fırat
Cancer remains a critical global health challenge, necessitating advanced drug delivery systems through innovations in materials science and nanotechnology. This study evaluates gadolinium metal-organic frameworks (Gd-MOFs) as potential drug delivery systems for anticancer therapy, particularly when combined with radiotherapy. Gd-MOFs were synthesized using terephthalic acid and gadolinium (III) chloride hexahydrate and then loaded with methotrexate (MTX). Characterization via fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), magnetic resonance imaging (MRI), and X-ray diffraction (XRD) confirmed their correct structure and stability. Effective MTX loading and controlled release were demonstrated. Anticancer effects were assessed on human healthy bronchial epithelial cells (BEAS-2B) and human lung cancer cells (A549) using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay under in vitro radiation therapy. MTX/Gd-MOF combined with radiotherapy showed a greater reduction in cancer cell viability (41.89% ± 2.75 for A549) compared to healthy cells (56.80% ± 1.97 for BEAS-2B), indicating selective cytotoxicity. These findings highlight the potential of Gd-MOFs not only as drug delivery vehicles but also as radiosensitizers, enhancing radiotherapy efficacy and offering promising evidence for their use in combinatory cancer therapies to improve treatment outcomes.
Investigation of the tissue equivalence of typical 3D-printing materials for application in internal dosimetry using monte carlo simulations
(2025) Karadeniz-Yıldırım, Ayşe; Tanyıldızı-Kökkülünk, Handan
This study evaluates the dosimetric accuracy of PLA and ABS 3D-printed phantoms compared to real tissues using Monte Carlo simulations in radionuclide therapy. Materials and methods: A phantom representing average liver and lung volumes, with a 10 mm tumor mimic in the liver, was simulated for radioembolization using 1 mCi Tc-99 m and 1 mCi Y-90. The dose distribution (DD) was compared across PLA, ABS, and real organ densities. Results: For Tc-99 m, PLA showed a + 5.6% DD difference in the liver, and ABS showed - 35.3% and - 40.9% differences in the lungs. For Y-90, PLA had a + 1.7% DD difference in the liver, while ABS showed - 34.2% and - 34.9% differences in the lungs. Conclusion: In MC simulation, PLA is suitable for representing high-density tissues, while ABS is appropriate for simulating moderately low-density tissues.
A rapid UV/Vis assisted designing of benzodithiophene based polymers by machine learning to predict their light absorption for photovoltaics
(Elsevier B.V., 2025) Hassan, Abrar U.; Güleryüz, Cihat; Sumrra, Sajjad H.; Noreen, Sadaf; Mahmoud, Mohamed H.H.
As global energy demands escalate, developing high-performance photovoltaic (PV) materials through accelerated design methodologies is imperative. A machine learning (ML) assisted predictive models are used to accelerate the design of benzodithiophene (BDT)-based polymers for their PV applications. The current approach leverages a curated dataset of 191 compounds with experimental UV–Vis spectra, mapped to molecular electronic descriptors via RDKit. Random Forest modeling yields a predictive framework (R2 = 0.98) for predicting their maximum absorption (λmax). After it, their 5000 new designs as novel polymers, identifying top performers with Synthetic Accessibility Likelihood Index scores up to 57, ensuring synthesis feasibility have also been designed. Feature importance analysis highlights MaxPartialCharge and Aromatic rings as crucial descriptors. The designed materials exhibit optimal energy gaps (1.35–2.0 eV), paving the way for efficient PV devices. The computed UV–Vis spectra of best predicted polymers are studied with their λmax range of 487–987 nm showing a significant redshift behavior. The designed polymers presents and good potential towards and they can be good candidates for organic solar cell applications.
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.
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.
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
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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