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Article summary:
1. GIAO NMR calculation is a powerful tool for structural assignment of organic molecules, and 13C NMR calculation is more accurate than 1H NMR calculation.
2. Previous studies have focused on achieving accurate theoretic 13C NMR calculation, such as the level of theory and basis sets used, and methods to process calculated shielding tensors.
3. This article proposes a new GIAO 13C NMR calculation protocol called sorted training set strategy (STS) which sorts carbons by their solvation radii, hybridization types, and substitution groups to obtain linear parameters for each group and cancel errors caused by discrepancies between types of carbons.
Article analysis:
This article provides an overview of the current state of GIAO 13C NMR calculation with respect to accuracy and reliability for structural assignation. The authors propose a new GIAO 13C NMR calculation protocol called sorted training set strategy (STS) which sorts carbons by their solvation radii, hybridization types, and substitution groups to obtain linear parameters for each group and cancel errors caused by discrepancies between types of carbons. The authors provide evidence that this method yields better results than global scaling correction (GSC).
The article is generally reliable in its presentation of the current state of GIAO 13C NMR calculation with respect to accuracy and reliability for structural assignation. The authors provide evidence from previous research studies that support their claims about the efficacy of their proposed STS method compared to GSC. They also provide evidence from artificially constructed structures that demonstrate the superiority of B3LYP-D3(BJ)/TZVP over RI-MP2/def2-SVP//B3LYP-D3(BJ)/TZVP in terms of accuracy. Furthermore, they present data from benchmark tests that show the superiority of STS over GSC in terms of accuracy when applied to “difficult” carbons such as carboxyl groups.
The article does not appear to be biased or one-sided in its presentation; it presents both sides equally without any promotional content or partiality towards either side. It also does not appear to contain any unsupported claims or missing points of consideration; all claims are supported by evidence from previous research studies or benchmark tests conducted by the authors themselves. Additionally, all possible risks associated with using this method are noted in the article.