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Enabling materials informatics for 29 Si solid-state NMR of crystalline materials
npj Computational Materials ( IF 9.7 ) Pub Date : 2020-05-12 , DOI: 10.1038/s41524-020-0328-3
He Sun , Shyam Dwaraknath , Handong Ling , Xiaohui Qu , Patrick Huck , Kristin A. Persson , Sophia E. Hayes

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for obtaining precise information about the local bonding of materials, but difficult to interpret without a well-vetted dataset of reference spectra. The ability to predict NMR parameters and connect them to three-dimensional local environments is critical for understanding more complex, long-range interactions. New computational methods have revealed structural information available from 29Si solid-state NMR by generating computed reference spectra for solids. Such predictions are useful for the identification of new silicon-containing compounds, and serve as a starting point for determination of the local environments present in amorphous structures. In this study, we have used 42 silicon sites as a benchmarking set to compare experimentally reported 29Si solid-state NMR spectra with those computed by CASTEP-NMR and Vienna Ab Initio Simulation Program (VASP). Data-driven approaches enable us to identify the source of discrepancies across a range of experimental and computational results. The information from NMR (in the form of an NMR tensor) has been validated, and in some cases corrected, in an effort to catalog these for the local spectroscopy database infrastructure (LSDI), where over 10,000 29Si NMR tensors for crystalline materials have been computed. Knowledge of specific tensor values can serve as the basis for executing NMR experiments with precision, optimizing conditions to capture the elements accurately. The ability to predict and compare experimental observables from a wide range of structures can aid researchers in their chemical assignments and structure determination, since the computed values enables the extension beyond tables of typical chemical shift (or shielding) ranges.



中文翻译:

晶体材料的29 Si固态NMR的材料信息学

核磁共振(NMR)光谱是获得有关材料局部键合的精确信息的强大工具,但是如果没有经过严格审查的参考光谱数据集,则很难解释。预测NMR参数并将其连接到三维局部环境的能力对于理解更复杂的远程相互作用至关重要。新的计算方法揭示了29种可用的结构信息通过生成固体的参考光谱来生成Si固态NMR。这样的预测可用于鉴定新的含硅化合物,并作为确定非晶结构中局部环境的起点。在这项研究中,我们使用了42个硅站点作为基准,以比较实验报告的29个Si固态NMR光谱,以及由CASTEP-NMR和Vienna Ab Initio Simulation Program(VASP)计算的那些。数据驱动的方法使我们能够确定一系列实验和计算结果中差异的根源。来自NMR的信息(以NMR张量的形式)已经过验证,在某些情况下已得到纠正,以便为本地光谱数据库基础结构(LSDI)进行分类,其中10,000多个29已经计算出晶体材料的Si NMR张量。特定张量值的知识可作为精确执行NMR实验,优化条件以准确捕获元素的基础。从各种结构预测和比较实验可观察物的能力可以帮助研究人员进行化学分配和结构确定,因为计算值可以扩展到超出典型化学位移(或屏蔽)范围表的范围。

更新日期:2020-05-12
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