This study describes the synthesis and thorough identification of N-[4-[4-[2-(2-hydroxyphenyl)-4,5-diphenyl-1H-imidazole-1-yl]phenyl]sulfonylphenyl]acetamide (NHDIPSA) using DFT-based computerized methods and experimental spectroscopy (liquid chromatography mass spectrometry, FT-Raman/IR, 13C NMR, and 1H NMR analysis). To evaluate its chemical reactivity, FMO and MEP surface analyses were performed, while topological studies (ELF, LOL, and NCI) elucidated the compound's intermolecular interactions. Computational PES analysis is carried out to understand the relationship between molecule energies and their geometrical structures. Vibrational and NMR assignments obtained at the B3LYP/6-311G(d,p) level showed close agreement with experimental FT-IR, FT-Raman, and NMR data, with RMSD values of 0.45–3.16 ppm for NMR chemical shifts. Docking simulations returned binding energies between −5.49 and −10.45 kcal mol−1 across the five target proteins, indicating favourable structural complementarity. Together, these results establish a validated structural and electronic profile for NHDIPSA and identify candidate protein targets for future experimental follow-up. No enzyme inhibition, antimicrobial, or cell-based assays were performed in this study; the biological terms used here refer to computationally predicted target compatibility, and experimental validation is left for future work.
R. Geetha, V. Balachandran, R. Arulraj et al.· RSC Advances· 0 citations
The rapid advancement of spintronic technologies has intensified the demand for magnetic materials with precisely engineered properties, including high spin polarization, large magnetic moments, tunable exchange interactions, and robust thermal stability. However, the simultaneous optimization of these properties remains challenging due to the vast compositional, structural, and interfacial design space governing magnetic systems. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for accelerating materials discovery and property prediction. This review provides an overview of recent progress in ML-driven spintronic materials research, focusing on how data-driven models, integrated with high-throughput first-principles calculations and experimental databases, are transforming the prediction of key magnetic properties. Advances in descriptor engineering, spanning compositional, structural, and electronic features, are discussed alongside emerging approaches such as graph neural networks and physics-informed learning. Key material classes, including Heusler alloys, topological spin systems, and two-dimensional magnets, are highlighted in the context of AI-assisted screening and inverse design. The review concludes by outlining future directions toward physics-guided, uncertainty-aware, and autonomous discovery frameworks that may enable closed-loop optimization of next-generation spintronic materials.
R. Elilarassi, D. Sivanandakumar, Palaniappan Nagarajan et al.· RSC Advances· 0 citations
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