Solvent Matters: Bridging Theory and Experiment in Quantum-Mechanical NMR Structural Elucidation

Abstract

Quantum-mechanical NMR (QM-NMR) is widely used in structure elucidation. A long-sought holey grail in this field is solving structures from a simple 1H NMR spectrum with AI-driven workflows. Yet, solvent effects on chemical shifts, though long recognized, remain overlooked. We show in a theory–experiment study that implicit solvation models miss solvent-induced variations and introduce a Python tool to quantify solvent sensitivity, aiding more reliable QM-NMR structural assignments.