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ABSTRACT This work implements a methodology for studying quadrupolar nuclear spin relaxation in ionic liquids. The dynamic behavior of the ions in the liquid is described by ab initio molecular dynamics (aiMD) with forces obtained from density functional theory (DFT) calculations with periodic boundary conditions and a non‐hybrid functional. The electric field gradient (EFG) driving the quadrupolar relaxation was calculated with free boundary conditions, using clusters that contained the ion of interest surrounded by two coordination shells treated quantum mechanically and augmented with a solvation model. Tests showed that EFG calculations using only the first coordination shell, containing five nearest neighbors, also provide a suitable model, because the relaxation rates differ by no more than 4% from the results from the two‐shell solvation. The results of this study show that the relaxation of the deuterated ethylammonium nitrate () occurs within the extreme narrowing regime for a spectrometer magnetic field T and is therefore characterized by the ensemble variance of the EFG and the correlation time associated with the EFG autocorrelation function. The quadrupolar relaxation of demanded molecular dynamics production times longer than 330 ps and averaging over multiple ions, as well as independent trajectories to get suitably converged relaxation rates. The calculated relaxation rate is Hz, about 60% above the rate reported experimentally. However, the approach utilized in the present study has an accuracy similar to, or better than, what has been previously reported for systems involving non‐ionic solvents that required simulations of 100 ps duration or less.
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Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT
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 27Al, 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 27Al crystalline solids using DFT and benchmarked them against 105 experimentally measured 27Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R2 value of 0.98 and an RMSE of 0.61 MHz for CQ, 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 27Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction.
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- PAR ID:
- 10629814
- Publisher / Repository:
- Scientific Reports (Nature)
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 15
- ISSN:
- 2045-2322
- Page Range / eLocation ID:
- 26456
- Subject(s) / Keyword(s):
- 27Al NMR NMR crystallography electric field gradient tensor density functional theory machine learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41598-025-10017-x
