A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F25%3A10489934" target="_blank" >RIV/00216208:11310/25:10489934 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UZ7y~89n_Z" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UZ7y~89n_Z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1039/d4fd00100a" target="_blank" >10.1039/d4fd00100a</a>
Alternative languages
Result language
angličtina
Original language name
A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR
Original language description
One of the main limitations in supporting experimental characterization of Al siting/pairing via modelling is the high computational cost of ab initio calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the ab initio (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the (23)Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the (23)Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Si(n)-Al sequence length. Furthermore, neglect of thermal and dynamical contributions was found to lead to errors of several ppm, and has a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for the (23)Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect of dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Faraday Discussions
ISSN
1359-6640
e-ISSN
1364-5498
Volume of the periodical
255
Issue of the periodical within the volume
January
Country of publishing house
GB - UNITED KINGDOM
Number of pages
26
Pages from-to
46-71
UT code for WoS article
001331539100001
EID of the result in the Scopus database
2-s2.0-85206517332