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The importance of nuclear quantum effects for NMR crystallography

Edgar A. Engel1*, Venkat Kapil2,3*, Michele Ceriotti3*

1 Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge, CB3 0HE UK

2 Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK

3 Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Vaud, Switzerland

* Corresponding authors emails: eae32@cam.ac.uk, vk380@cam.ac.uk, michele.ceriotti@epfl.ch
DOI10.24435/materialscloud:nj-2g [version v1]

Publication date: Jul 23, 2021

How to cite this record

Edgar A. Engel, Venkat Kapil, Michele Ceriotti, The importance of nuclear quantum effects for NMR crystallography, Materials Cloud Archive 2021.118 (2021), doi: 10.24435/materialscloud:nj-2g.


The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of the computational prediction of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy surface. To test the limits of this approximation, we perform a systematic study of the role of finite-temperature and quantum nuclear fluctuations on 1H, 13C, and 15N chemical shieldings in molecular crystals -- considering the paradigmatic examples of the different polymorphs of benzene, glycine, and succinic acid. We find the effect of quantum fluctuations to be comparable in size to the typical errors of predictions of chemical shieldings for static nuclei with respect to experimental measurements, and to improve the match between experiments and theoretical predictions, translating to more reliable assignment of the NMR spectra to the correct candidate structure. Thanks to the use of integrated machine-learning models trained on both first-principles configurational energies and chemical shieldings, the accurate sampling of thermal and quantum fluctuations of the structures can be achieved at an affordable cost, setting a new standard for the calculations that underlie solid-state structural determination by NMR. This archive contains the machine-learning potentials and shielding models, sample inputs, and scripts to reproduce the results discussed in the manuscript. It further contains the reference data and scripts to reconstruct the shielding models.

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File name Size Description
6.9 KiB README file detailing the contents of the data archive
1.7 GiB Archive containing the first-principles reference data, machine-learning potentials and shielding models, sample inputs, and scripts to reproduce the results discussed in the associated manuscript, as detailed in the supplied README file


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Paper detailing the methodology and accordingly obtained results.)
E. A. Engel, V. Kapil, M. Ceriotti, Journal of Physical Chemistry Letters (submitted)


molecular crystals NMR chemical shielding nuclear quantum effects first-principles GIPAW DFT machine learning SNSF MARVEL

Version history:

2021.118 (version v1) [This version] Jul 23, 2021 DOI10.24435/materialscloud:nj-2g