Benchmark fragment-based (1)H, (13)C, (15)N and (17)O chemical shift predictions in molecular crystals.
Benchmark fragment-based (1)H, (13)C, (15)N and (17)O chemical shift predictions in molecular crystals.
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DOI：
10.1039/c6cp01831a
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发表时间：
2016-08-21
期刊：
影响因子：
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通讯作者：
Beran GJ
中科院分区：
文献类型：
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作者：
Hartman JD;Kudla RA;Day GM;Mueller LJ;Beran GJ
基于片段的从头算1H，13 C，15 N和17 O化学位移预测的性能进行评估对实验NMR化学位移数据在四个基准组的分子晶体。采用各种常用的密度泛函（PBE 0，B3 LYP，TPSSh，OPBE，PBE，TPSS），我们探讨了簇，两体碎片，组合簇/碎片模型的相对性能。混合密度泛函（PBE 0，B3 LYP和TPSSh）通常优于其基于广义梯度近似（GGA）的对应物。1H，13 C，15 N和17 O各向同性化学位移可以预测的均方根误差分别为0.3，1.5，4.2和9.8 ppm，使用计算成本低廉的静电嵌入的两体PBE 0片段模型。氧化学屏蔽被证明对局部多体效应特别敏感，并且使用组合的簇/碎片模型而不是简单的两体碎片模型将均方根误差降低到7.6 ppm。对于相同的1H、13 C、15 N和17 O测试集，这些基于碎片的模型误差与GIPAW PBE的0.4、2.2、5.4和7.2 ppm误差相比毫不逊色。使用这些基准计算，一组推荐的线性回归参数计算的化学屏蔽和观察到的化学位移之间的映射，并使用统计交叉验证评估其鲁棒性。我们证明了这些方法的实用性和报告的缩放参数的应用9-叔丁基蒽，几个组氨酸共晶体，苯甲酸和C-亚硝基芳烃SnCl 2（CH 3）2（NODMA）2。
The performance of fragment-based ab initio 1H, 13C, 15N and 17O chemical shift predictions is assessed against experimental NMR chemical shift data in four benchmark sets of molecular crystals. Employing a variety of commonly used density functionals (PBE0, B3LYP, TPSSh, OPBE, PBE, TPSS), we explore the relative performance of cluster, two-body fragment, and combined cluster/fragment models. The hybrid density functionals (PBE0, B3LYP and TPSSh) generally out-perform their generalized gradient approximation (GGA)-based counterparts. 1H, 13C, 15N, and 17O isotropic chemical shifts can be predicted with root-mean-square errors of 0.3, 1.5, 4.2, and 9.8 ppm, respectively, using a computationally inexpensive electrostatically embedded two-body PBE0 fragment model. Oxygen chemical shieldings prove particularly sensitive to local many-body effects, and using a combined cluster/fragment model instead of the simple two-body fragment model decreases the root-mean-square errors to 7.6 ppm. These fragment-based model errors compare favorably with GIPAW PBE ones of 0.4, 2.2, 5.4, and 7.2 ppm for the same 1H, 13C, 15N, and 17O test sets. Using these benchmark calculations, a set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided and their robustness assessed using statistical cross-validation. We demonstrate the utility of these approaches and the reported scaling parameters on applications to 9-tertbutyl anthracene, several histidine co-crystals, benzoic acid and the C-nitrosoarene SnCl2(CH3)2(NODMA)2.