Ab-initio Quality NMR Parameters in Solid-State Materials using a High-Dimensional Neural-Network Representation
Hassanali, Ali A
Journal of Chemical Theory and Computation
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Cuny, J., Xie, Y., Pickard, C., & Hassanali, A. A. (2016). Ab-initio Quality NMR Parameters in Solid-State Materials using a High-Dimensional Neural-Network Representation. Journal of Chemical Theory and Computation, 12 765-773. https://doi.org/10.1021/acs.jctc.5b01006
Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful exper- imental tools to probe the local atomic order of a wide range of solid-state compounds. However, due to the complexity of the related spectra, in particular for amorphous materials, their interpretation in terms of structural information is often challenging. These difficulties can be overcome by combining molecular dynamics simulations to generate realistic structural models with an ab initio evaluation of the corresponding nuclear shielding and quadrupolar coupling tensors. However, due to computational constraints, this approach is limited to relatively small system sizes which, for amor- phous materials, prevents an adequate statistical sampling of the distribution of the local environments that is required to quantitatively describe the system. In this work, we present an approach to efficiently and accurately predict the NMR parameters of very large systems. This is achieved by using a high-dimensional neural-network rep- resentation of NMR parameters that are calculated using an ab initio formalism. To illustrate the potential of this approach, we applied this neural-network NMR (NN- NMR) method on the ¹⁷O and ²⁹Si quadrupolar coupling and chemical shift parameters of various crystalline silica polymorphs and silica glasses. This approach is, in principal, general and has the potential to be applied to predict the NMR properties of various materials.
External DOI: https://doi.org/10.1021/acs.jctc.5b01006
This record's URL: https://www.repository.cam.ac.uk/handle/1810/253217