NMR crystallography for validating organic crystal structures

密度泛函理论代码的发展意味着现在可以相对简单地计算给定晶体结构的核磁共振（NMR）参数，例如化学位移。在最近研究剑桥结构数据库中重复结构测定的工作中，我们发现许多情况下需要额外的实验证据，例如来自NMR的证据，以区分来自衍射研究的替代解决方案。考虑到NMR核的多样性，NMR可观测量的多样性（例如，各向同性化学位移、四极耦合等），以及可用的1D和2D NMR实验的多样性，然而，哪种NMR实验最有效地区分正确和不正确的结构并不明显。一些方法来验证晶体结构将进行评估，从简单的措施之间的偏差实验和计算的变化，以更复杂的方法，如贝叶斯推理，以确定定量的信息提供的不同的数据集。我们还将利用机器学习方法中的本地专业知识来识别结果中的模式，因为不同的情况可能需要不同的实验输入。

Developments in Density Functional Theory codes means that it is now relatively straightforward to calculate Nuclear Magnetic Resonance (NMR) parameters, such as chemical shifts, for a given crystal structure. In recent work examining repeat structure determinations in the Cambridge Structural Database, we have found many cases where additional experimental evidence, e.g. from NMR, is needed to distinguish between alternative solutions derived from diffraction studies. Given the variety of NMR nuclei, the variety of NMR observables (e.g. isotropic chemical shifts, quadrupolar couplings etc.), and the variety of 1D and 2D NMR experiments available it is not obvious, however, which NMR experiments distinguish most effectively between correct and incorrect structures. A number of approaches to validating crystal structures will be evaluated, ranging from simple measures of deviations between experimental and calculated shifts to more sophisticated methods e.g. Bayesian inference to determine quantitatively the information provided by different data sets. We will also exploit local expertise in machine learning approaches to identify patterns in results, since it is likely that different situations will require different experimental inputs.