Sohail Hamid Dar is a postdoctoral researcher at the Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), supervised by Prof. Jun Zhu. His work sits at the intersection of computational chemistry, adaptive aromaticity, dinitrogen activation, and machine learning for chemical property prediction. He completed his Ph.D. in Chemistry at Pondicherry University, India, under Prof. Musiri M. Balakrishnarajan. His dissertation — On the Aberrant Anatomy of Classical Bonding Between Boron Atoms: From Molecules to Solids — examined the electronic structure and bonding of main-group systems using DFT, EDA, QTAIM, NBO, and FMO theory. Before joining CUHK-Shenzhen, he held a postdoctoral appointment at the Indian Institute of Science (IISc), Bangalore, with Prof. E. D. Jemmis, where he studied interlayer bonding in B₄₈ bilayer nanoclusters using periodic DFT. His research is driven by a central question: why do molecules and materials behave the way they do? He addresses this through quantum chemical analysis—combining molecular and periodic DFT, frontier molecular orbital theory, and bonding decomposition schemes to rationalize electronic effects that resist intuitive explanation. His computational toolkit spans VASP, CASTEP, Gaussian, Quantum ESPRESSO, YaEHMOP, Multiwfn, NBO 7.0, Materials Studio, and Python-based ML workflows.