Abstract:

Supramolecular chemistry, focusing on non-covalent interactions between molecules, has become a key area of research in understanding complex biological systems and material science. The study of supramolecular forces—such as hydrogen bonding, van der Waals interactions, and π–π stacking—has opened new avenues for developing molecular assemblies and functional materials. However, these interactions are often subtle and difficult to quantify. Statistical methods, including regression analysis, machine learning, and computational simulations, offer powerful tools to interpret and predict these non-covalent interactions. This paper explores the integration of statistical techniques in unraveling supramolecular forces, discusses current advancements, and highlights the potential for future research in this field.

Description:

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