The importance of nuclear quantum effects for NMR crystallography

TL;DR

This paper demonstrates that quantum nuclear effects significantly influence NMR chemical shieldings in molecular crystals, and incorporating these effects via machine learning enhances the accuracy of NMR crystallography.

Contribution

It introduces a method to include quantum nuclear fluctuations in NMR shielding predictions using machine learning, improving structural assignments.

Findings

Quantum fluctuations affect NMR shieldings comparably to static prediction errors.

Including quantum effects improves agreement with experimental NMR data.

Machine learning enables efficient sampling of nuclear fluctuations.

Abstract

The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy. To test the limits of this approximation, we systematically study the importance of finite-temperature and quantum nuclear fluctuations for $^1$H, $^{13}$C, and $^{15}$N shieldings in polymorphs of three paradigmatic molecular crystals -- benzene, glycine, and succinic acid. The effect of quantum fluctuations is comparable to the typical errors of shielding predictions for static nuclei with respect to experiments, and their inclusion to improve the agreement with measurements, translating to more reliable assignment of the NMR spectra to the correct candidate structure. The use of integrated machine-learning models, trained on first-principles energies and shieldings, renders rigorous sampling of nuclear fluctuations affordable, setting a new standard for the calculations underlying NMR structure determinations.