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dc.contributor.authorCuny, Jérômeen
dc.contributor.authorXie, Yuen
dc.contributor.authorPickard, Christopheren
dc.contributor.authorHassanali, Ali Aen
dc.date.accessioned2016-01-13T12:49:10Z
dc.date.available2016-01-13T12:49:10Z
dc.date.issued2016-01-05en
dc.identifier.citationJ. Cuny et al. Journal of Chemical Theory and Computation (2016). volume 12, issue 2: pp. 765-773. doi:10.1021/acs.jctc.5b01006en
dc.identifier.issn1549-9618
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/253217
dc.description.abstractNuclear 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.
dc.languageEnglishen
dc.language.isoenen
dc.publisherACS
dc.titleAb-initio Quality NMR Parameters in Solid-State Materials using a High-Dimensional Neural-Network Representationen
dc.typeArticle
dc.description.versionThis is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acs.jctc.5b01006en
prism.endingPage773
prism.publicationDate2016en
prism.publicationNameJournal of Chemical Theory and Computationen
prism.startingPage765
prism.volume12en
rioxxterms.versionofrecord10.1021/acs.jctc.5b01006en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2016-01-05en
dc.contributor.orcidPickard, Christopher [0000-0002-9684-5432]
dc.identifier.eissn1549-9626
rioxxterms.typeJournal Article/Reviewen
rioxxterms.freetoread.startdate2017-01-05


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