TCM Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom.
Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
J Phys Chem Lett. 2021 Aug 19;12(32):7701-7707. doi: 10.1021/acs.jpclett.1c01987. Epub 2021 Aug 6.
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 H, C, and 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 improves 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.
固态核磁共振(NMR)晶体学的分辨率在很大程度上取决于对候选结构的 NMR 化学位移的计算预测的准确性,这些预测通常被认为是势能中的局部最小值。为了测试这种近似的极限,我们系统地研究了有限温度和量子核涨落对三种典型分子晶体(苯、甘氨酸和琥珀酸)中多晶型物的 H、C 和 N 屏蔽的重要性。量子涨落的影响与实验中静态核屏蔽预测的典型误差相当,并且它们的包含提高了与测量结果的一致性,从而更可靠地将 NMR 谱分配给正确的候选结构。使用基于第一性原理能量和屏蔽的集成机器学习模型,使核涨落的严格采样变得可行,为 NMR 结构确定的计算设定了新标准。