Machine learning of Al NMR electric field gradient tensors for crystalline structures from DFT.
Journal:
Scientific reports
Published Date:
Jul 21, 2025
Abstract
NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG) tensor. The non-negligible computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors from 1681 Al crystalline solids using DFT and benchmarked them against 105 experimentally measured Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R value of 0.98 and an RMSE of 0.61 MHz for C, the quadrupolar coupling constant. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction.
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