Combining double-hybrid functionals with rSCAN yields solid-state ¹³C chemical shifts with sub-ppm accuracy

Area of Science:

• Computational Chemistry
• Solid-State NMR Spectroscopy
• NMR Crystallography

Background:

• Accurate prediction of NMR shieldings is crucial for spectral assignment and crystal structure refinement in NMR crystallography.
• Periodic calculations commonly use GGA functionals (PBE, BLYP), but a monomer correction can improve local electronic structure treatment.
• The meta-GGA functional rSCAN shows promise for geometry optimization, but its performance in periodic shielding calculations is largely untested.

Purpose of the Study:

• To evaluate the performance of the rSCAN functional in periodic NMR shielding calculations.
• To assess the impact of monomer corrections and double-hybrid functionals on shielding prediction accuracy.
• To identify the optimal computational method for accurate 13C chemical shift prediction in molecular solids.

Main Methods:

• Calculated NMR shieldings using rSCAN and PBE functionals on geometries optimized by rSCAN, PBE, and BLYP.
• Applied monomer corrections to calculated shieldings.
• Investigated the use of double-hybrid functionals (mPW2PLYP) with implicit solvation models (CPCM) for monomer corrections.

Main Results:

• rSCAN provided marginally improved geometries but less accurate chemical shifts than PBE initially.
• Monomer-corrected rSCAN outperformed corrected PBE, indicating better description of long-range shielding contributions.
• The mPW2PLYP-CPCM correction applied to rSCAN periodic calculations on rSCAN-optimized geometries achieved unprecedented accuracy (0.8 ppm RMSE, 0.6 ppm MAE) on a large dataset.

Conclusions:

• The rSCAN functional, when combined with monomer correction and a double-hybrid functional (mPW2PLYP-CPCM), significantly enhances the accuracy of periodic NMR shielding calculations.
• This optimized method provides highly reliable 13C chemical shift predictions for molecular solids, advancing NMR crystallography.
• The study highlights the importance of advanced functionals and correction schemes for accurate computational predictions in solid-state NMR.