Aluminium siting in zeolite RTH from a combined machine learning – NMR approach

Abstract

Determining the distribution of aluminium in zeolite frameworks remains a significant challenge, due to the limited sensitivity of conventional characterization techniques. To overcome this issue, we have developed a procedure which combines experimental two-dimensional (2D) solid-state NMR spectroscopy with machine learning (ML) techniques. To validate the approach, we have applied it to achieve a detailed assignment of Al environments in zeolite RTH. A reactive ML potential was used to model long-timescale framework dynamics under realistic conditions, including temperature and hydration, alongside the accurate prediction of isotropic NMR chemical shifts. Comparison between theoretical and experimental spectra reveals that Al preferentially occupies the T₂ sites, with under-population of the other T-sites. The excellent agreement between computed and observed NMR data demonstrates that this ML-augmented spectroscopic approach is a powerful tool for quantitative elucidation of Al distributions in structurally complex zeolites, going far beyond the limitations of traditional quantum chemical approaches.