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

Area of Science:

• Computational Chemistry
• Nuclear Magnetic Resonance Spectroscopy
• Artificial Intelligence in Chemistry

Background:

• Quantum-mechanical Nuclear Magnetic Resonance (QM-NMR) is crucial for chemical structure elucidation.
• AI-driven workflows aim to solve structures directly from ¹H NMR spectra.
• Solvent effects on NMR chemical shifts are known but often inadequately addressed in computational models.

Purpose of the Study:

• To investigate the impact of solvent effects on chemical shifts in QM-NMR.
• To evaluate the performance of implicit solvation models in capturing solvent-induced variations.
• To develop a computational tool for assessing solvent sensitivity in QM-NMR.

Main Methods:

• Theoretical calculations using quantum mechanics to model NMR chemical shifts.
• Comparison of theoretical predictions with experimental data across different solvents.
• Development and application of a Python tool to quantify solvent sensitivity.

Main Results:

• Implicit solvation models fail to accurately capture significant solvent-induced variations in chemical shifts.
• Solvent sensitivity varies considerably among different molecular structures.
• The developed Python tool effectively quantifies solvent sensitivity.

Conclusions:

• Accurate QM-NMR structure elucidation requires explicit consideration of solvent effects.
• Existing implicit solvation models are insufficient for reliable AI-driven NMR spectral analysis.
• The new tool enhances the reliability of QM-NMR assignments by accounting for solvent sensitivity.