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

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.