27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F25%3A10490545" target="_blank" >RIV/00216208:11310/25:10490545 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZvW97vT8mf" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZvW97vT8mf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1039/d4dd00306c" target="_blank" >10.1039/d4dd00306c</a>

Result language

angličtina

Original language name

27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction

Original language description

Zeolites, such as MFI, are versatile microporous aluminosilicate materials that are widely used in catalysis and adsorption processes. The location and the character of the aluminium within the zeolite framework is one of the important determinants of performance in industrial applications, and is typically probed by (27)Al NMR spectroscopy. However, interpretation of (27)Al NMR spectra is challenging, as first-principles computational modelling struggles to achieve the timescales and model complexity needed to provide reliable assignments. In this study, we deploy advanced machine learning-based methods to help bridge the time and model complexity scale by first utilizing neural network interatomic potentials to achieve significant speed-up in structure sampling compared to traditional density functional theory (DFT) approaches, and second by training regression models to cost-effectively predict the (27)Al chemical shifts. This allows us, for the H-MFI zeolite as a use case, to comprehensively explore the effect of various conditions relevant to catalysis, including water loading, temperature, and the aluminium concentration, on the (27)Al chemical shifts. We demonstrate that both water content and temperature significantly affect the chemical shift and do so in a non-trivial way that is highly T-site dependent, highlighting a need for adoption of realistic, case-specific models. We also observe that our approach is able to achieve close to quantitative agreement with relevant experimental data for such a complex zeolite as MFI, allowing for the tentative assignment of the experimental NMR peaks to specific T-sites. These findings provide a testament to the capabilities of machine learning approaches in providing reliable predictions of important spectroscopic observables for complex industrially relevant materials under realistic conditions.

Czech name

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Czech description

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Type

J_imp - 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

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)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Digital Discovery

ISSN

2635-098X

e-ISSN

2635-098X

Volume of the periodical

4

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

275-288

UT code for WoS article

001378563300001

EID of the result in the Scopus database

2-s2.0-85212710843