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分子固态 NMR 屏蔽常数的高精度电子结构计算。

High Level Electronic Structure Calculation of Molecular Solid-State NMR Shielding Constants.

机构信息

Institut des Sciences Chimiques de Rennes, Av. Général Leclerc, 357000 Rennes, France.

Max-Planck-Institut für Kohlenforschung, 45470 Mülheim an der Ruhr, Germany.

出版信息

J Chem Theory Comput. 2022 Apr 12;18(4):2408-2417. doi: 10.1021/acs.jctc.1c01095. Epub 2022 Mar 30.

DOI:10.1021/acs.jctc.1c01095
PMID:35353527
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9009078/
Abstract

In this work, we present a quantum mechanics/molecular mechanics (QM/MM) approach for the computation of solid-state nuclear magnetic resonance (SS-NMR) shielding constants (SCs) for molecular crystals. Besides applying standard-DFT functionals like GGAs (PBE), meta-GGAs (TPSS), and hybrids (B3LYP), we apply a double-hybrid (DSD-PBEP86) functional as well as MP2, using the domain-based local pair natural orbital (DLPNO) formalism, to calculate the NMR SCs of six amino acid crystals. All the electronic structure methods used exhibit good correlation of the NMR shieldings with respect to experimental chemical shifts for both H and C. We also find that local electronic structure is much more important than the long-range electrostatic effects for these systems, implying that cluster approaches using all-electron/Gaussian basis set methods might offer great potential for predictive computations of solid-state NMR parameters for organic solids.

摘要

在这项工作中,我们提出了一种量子力学/分子力学(QM/MM)方法,用于计算分子晶体的固态核磁共振(SS-NMR)屏蔽常数(SCs)。除了应用标准密度泛函理论(DFT)泛函,如广义梯度近似(GGA)(PBE)、meta-GGA(TPSS)和混合泛函(B3LYP)外,我们还应用了双杂交(DSD-PBEP86)泛函以及 MP2 方法,使用基于域的局部对自然轨道(DLPNO)形式,来计算六种氨基酸晶体的 NMR SCs。所有使用的电子结构方法都表现出与 H 和 C 的实验化学位移之间的 NMR 屏蔽的良好相关性。我们还发现,对于这些体系,局部电子结构比远程静电效应重要得多,这意味着使用全电子/高斯基方法的簇方法可能为有机固体的固态 NMR 参数的预测计算提供巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/e5b75c3996b4/ct1c01095_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/964e8b3664e8/ct1c01095_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/2053cedd8be1/ct1c01095_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/c3732469ee0e/ct1c01095_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/cc87848a7f35/ct1c01095_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/e5b75c3996b4/ct1c01095_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/964e8b3664e8/ct1c01095_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/2053cedd8be1/ct1c01095_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/c3732469ee0e/ct1c01095_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/cc87848a7f35/ct1c01095_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/9009078/e5b75c3996b4/ct1c01095_0005.jpg

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J Chem Theory Comput. 2021 Nov 9;17(11):6876-6885. doi: 10.1021/acs.jctc.1c00604. Epub 2021 Oct 12.
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DLPNO-MP2 second derivatives for the computation of polarizabilities and NMR shieldings.
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J Chem Phys. 2021 Apr 28;154(16):164110. doi: 10.1063/5.0047125.
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