An Accurate Tensorial Model for Prediction of Full Zeolite NMR Spectra

TL;DR

This paper introduces a tensorial machine learning model that accurately predicts complete NMR tensors for zeolites, enabling high-throughput and realistic ss-NMR spectrum simulations of complex crystalline materials.

Contribution

The work presents a novel tensorial machine learning approach that efficiently predicts full NMR tensors, reducing computational costs for zeolite spectrum simulations.

Findings

High-precision prediction of NMR tensors for diverse zeolitic materials

Successful translation of tensor predictions into full ss-NMR spectra

Demonstration of the model's utility on complex zeolite RTH

Abstract

Solid state nuclear magnetic resonance (ss-NMR) is one of the most sensitive and popular techniques for structure elucidation in geometrically complex crystalline materials, such as zeolites. Synergistic support from computational modelling is vital to interpret experimental spectra, and relate ss-NMR to atomistic models. Nevertheless, computational predictions are hindered by the high expense of calculating magnetic shielding (MS) and electric field gradient (EFG) tensors from first principles. In this work, we leverage a novel tensorial machine learning approach to train a general model for predicting complete NMR tensors. We demonstrate the utility of the approach for a diverse dataset of zeolitic materials and NMR-active nuclei ($^{27}$Al, $^{29}$Si, $^{17}$O, $^{23}$Na and $^{1}$H), predicting all NMR observables to a high degree of precision. These observables are then translated into predictions of the full $^{27}$Al and $^{29}$Si ss-nMR spectra for the exemplary zeolite RTH. Thus, this work opens a pathway to accurate, high-throughput NMR simulation for large-scale and realistic models of chemically complex zeolites.