Supramolecular Chemistry: Exploring the Use of Electronic Structure, Molecular Dynamics, and Machine Learning Approaches

Abstract

This review aims to highlight the role that computational chemistry has played in advancing the supramolecular chemistry field. We demonstrated recent uses of computational methodologies to elucidate noncovalent interactions in various processes occurring in supramolecular systems. We also emphasized the contributions of these techniques to studying reactions within confined space, showing how computational methodologies help clarify the effects of reactivity and conformational locking. Furthermore, we underscore the utilization of Molecular Dynamics (MD) in elucidating dynamical processes, understanding temperature and pressure effects, and exploring conformational space within supramolecular chemistry. Finally, we highlight the impact that the age of machine learning has on computational chemistry, showing how these universal approximators can enhance existing methods, predict properties, and efficiently explore the chemical space encompassed by these complex systems.