Improved Electrostatic Embedding for Fragment-Based Chemical Shift Calculations in Molecular Crystals

Area of Science:

• Computational Chemistry
• Solid-State Chemistry
• Spectroscopy

Background:

• Fragment-based methods are crucial for accurate prediction of NMR chemical shielding tensors in molecular crystals.
• Current methods rely on electrostatic embedding, which can be sensitive to the chosen treatment.
• Improving the embedding environment is key to enhancing the accuracy of these predictions.

Purpose of the Study:

• To develop an improved electrostatic embedding model for fragment-based NMR chemical shift calculations in molecular crystals.
• To incorporate self-consistently polarized Madelung field effects into fragment-based methods.
• To assess the performance of the new model for predicting various NMR chemical shifts.

Main Methods:

• Development of the Self-Consistent Reproduction of the Madelung Potential (SCRMP) model.
• Incorporation of self-consistent lattice polarization via an array of point charges.
• Assessment of SCRMP with density functionals (PBE, PBE0) for fragment- and cluster-based NMR chemical shift predictions (¹H, ¹³C, ¹⁴N, ¹⁷O).

Main Results:

• The SCRMP model demonstrated substantial improvements in predicted ¹⁷O chemical shifts.
• Modest improvements were observed for ¹⁵N chemical shifts.
• The model's efficacy was validated by assigning challenging oxygen chemical shifts in glycine's γ-polymorph.

Conclusions:

• The SCRMP model offers a significant enhancement to electrostatic embedding for NMR chemical shift predictions in molecular crystals.
• SCRMP-embedded NMR chemical shift predictions achieve accuracy comparable to or exceeding the widely used GIPAW model.
• This advancement provides a more reliable computational tool for analyzing solid-state NMR data.