Integrating Quantum Chemistry and Machine Learning for Accurate Modelling of Aromaticity, Hydrogen Bonding, and Metal Co-Factors

Aromaticity, hydrogen bonding, and metal cofactors are fundamental interactions governing the structure, stability, and function of biomolecular and catalytic systems. Their accurate computational representation remains a major challenge due to the combined influence of electron delocalization, polarization effects, and complex quantum mechanical behavior, particularly in transition-metal environments. Classical molecular mechanics force fields, while computationally efficient, fail to capture these phenomena reliably, motivating the development of quantum mechanical (QM), hybrid QM/MM, and machine-learning (ML) enhanced approaches. This article systematically reviews recent advances in the modelling of aromatic stabilization, hydrogen-bonding dynamics, and metal–ligand coordination using density functional theory (DFT), multi-scale QM/MM simulations, and modern ML potentials. Benchmark systems including aromatic hydrocarbons, hydrogen-bonded clusters, peptide fragments, and biologically relevant metal complexes were analyzed using dispersion-corrected DFT functionals and ML-based force fields trained on high-level QM datasets. Validation metrics such as interaction energies, geometric parameters, aromaticity indices, hydrogen-bond lifetimes, and metal-coordination stability were employed to assess predictive performance. The results demonstrate that modern DFT methods accurately reproduce electronic delocalization and interaction energetics, while QM/MM techniques effectively capture environmental effects in large biomolecular systems. Machine-learning potentials achieve near-QM accuracy at substantially reduced computational cost, showing strong performance for aromatic systems and hydrogen-bond networks, though challenges remain for redox-active metal centers and multi-reference electronic states. Overall, the study highlights that no single modelling strategy is universally optimal. Instead, integrated hybrid frameworks combining QM accuracy, ML efficiency, and classical scalability offer the most promising pathway toward predictive and interpretable simulations. Future progress will depend on metal-inclusive training datasets, physics-informed ML architectures, and improved treatment of polarization and electronic correlation to enable robust modeling across complex chemical space.