<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Engel, Edgar A.</dc:creator> <dc:creator>Kapil, Venkat</dc:creator> <dc:creator>Ceriotti, Michele</dc:creator> <dc:date>2021-07-23</dc:date> <dc:description>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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2021.118</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:nj-2g</dc:identifier> <dc:identifier>mcid:2021.118</dc:identifier> <dc:identifier>oai:materialscloud.org:957</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>molecular crystals</dc:subject> <dc:subject>NMR chemical shielding</dc:subject> <dc:subject>nuclear quantum effects</dc:subject> <dc:subject>first-principles GIPAW DFT</dc:subject> <dc:subject>machine learning</dc:subject> <dc:subject>SNSF</dc:subject> <dc:subject>MARVEL</dc:subject> <dc:title>The importance of nuclear quantum effects for NMR crystallography</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>