Machine learning of ²⁷Al NMR electric field gradient tensors for crystalline structures from DFT.

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 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 measur

SUBMITTER: Sun H 

PROVIDER: S-EPMC12280080 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature