Designing New Crystallizable Organic Semiconductors for Optoelectronic Applications: Insights From a Computational Study and Machine Learning Mohammed F. Hassan, Azal S. Waheeb, Zain Al‐Abidine N. A. Abbas, Hussein A. K. Kyhoiesh, Hassan E. Abd Elsalam, Mohamed H. H. Mahmoud, Islam H. El Azab Energy Technology, 2026 Organic semiconductors have revolutionized electronics, but their amorphous nature hinders performance and stability. Crystallinity overcomes these limitations; however, the design of materials that combine high crystallinity, optimal thermal properties, and ease of synthesis remains a significant challenge. A machine learning (ML)‐assisted approach combined with the density functional theory (DFT) study was employed to generate a vast chemical space of crystallizable organic semiconductors. By breaking retrosynthetically interesting chemical species (BRICS), over 1700 new organic semiconductors with promising synthetic accessibility (SA) were designed. ML algorithms, specifically extra trees (ET) and random forest, were used to predict the melting temperatures ( T m ) of these semiconductors, yielding good R ‐squared ( R 2 ) values of 0.94–0.96. Dimensionality reduction analysis reveals that the t‐distributed stochastic neighbor embedding (t‐SNE) components 1 and 2 of these semiconductors ranged within the same value (−5 to 5), indicating a high degree of similarity. Furthermore, analysis of SA showed that new organic semiconductors with SMILES lengths between 15 and 40 are more likely to be easily synthesized. Based on these findings, 20 new candidate semiconductors were identified for practical synthesis and analysis. DFT calculations were employed to study the optoelectronic properties of chromophores. This study demonstrates the power of ML‐assisted design in generating crystallizable organic semiconductors with enhanced SA. The findings are expected to contribute significantly to the development of high‐performance organic electronics.
Machine Learning-Guided Design of Organic Compounds With Tailored Surface Tension Using Molecular Fingerprints Hussein A. K. Kyhoiesh, Rihab A. H. Dubaish, Zaman A. I. Alaridhee, Hassan E. Abd Elsalam, Islam H. El Azab Chemistryselect, 2026 ABSTRACT Organic compounds play a crucial role in various industrial applications, and their properties, such as surface tension, significantly impact their performance. Designing new organic compounds with desired properties is a challenging task, and machine learning (ML) has emerged as a promising approach to accelerate this process. In this study, we developed an ML model to predict the surface tension of organic compounds using 2048‐bit fingerprints. Among various algorithms, XGBoost demonstrated the best performance with an R 2 score of 0.87. Leveraging this model, we designed 910 new organic compounds with surface tension values as high as 35. The data was visualized using SALI scatter plots, providing insights into the chemical space of the designed compounds. Furthermore, we calculated the synthetic accessibility scores for these compounds and identified 20 promising candidates for experimental synthesis. This work showcases the potential of ML in designing new organic compounds with desired properties, paving the way for accelerated materials discovery and development.
Efficient On-Demand Routing with LEACH Protocol in Mobile IoT Networks Waleed Hadi Madhloom Kurdi, Fatima Hashim Abbas, Megala Rajendran, Hussein Kadhim Sultan, Waleed Mohammed Khazaal, Kassem Al Attabi 2025 International Conference on Next Generation Computing Systems Intelligent System for Sustainable Development Icngcs 2025 Conference Proceedings, 2025
Synthesis, spectral characterization and biological activity of 2-[2- - (1-amino -1, 5- Dinitrophenyl) azo]-imidazole Journal of Global Pharma Technology, 2019
