Hassan, Abrar U.Güleryüz, CihatEl Azab, Islam H.Elnaggar, Ashraf Y.Mahmoud, Mohamed H.H.2025-03-232025-03-23202510.1016/j.optmat.2025.116783Hassan, A. U., Güleryüz, C., El Azab, I. H., Elnaggar, A. Y., & Mahmoud, M. H. (2025). Exploring the structural basis of crystals that affect nonlinear optical responses: an experimental and machine learning quest. Optical Materials, 116783.0925-3467https://hdl.handle.net/20.500.12939/5728Machine 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.eninfo:eu-repo/semantics/closedAccessBandgapDFTNonlinear optical responsePolymorphSingle crystalArticle1602-s2.0-85217021843WOS:001425705500001