ArticlePhysical chemistry chemical physics : PCCP2016
Benchmark fragment-based (1)H, (13)C, (15)N and (17)O chemical shift predictions in molecular crystals.
Article in Physical chemistry chemical physics : PCCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids.The journal of physical chemistry. C, Nanomaterials and interfaces · 2022Article
- Combining X-ray and NMR Crystallography to Explore the Crystallographic Disorder in Salbutamol Oxalate.Crystal growth & design · 2022Article
- High Level Electronic Structure Calculation of Molecular Solid-State NMR Shielding Constants.Journal of chemical theory and computation · 2022Article
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5 authors.
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Abstract
The performance of fragment-based ab initio(1)H, (13)C, (15)N and (17)O 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. (1)H, (13)C, (15)N, and (17)O 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 (1)H, (13)C, (15)N, and (17)O 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-tert-butyl anthracene, several histidine co-crystals, benzoic acid and the C-nitrosoarene SnCl2(CH3)2(NODMA)2.
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