High Throughput calculations and machine learning modeling of $^{17}\text{O}$ NMR in non-magnetic oxides

Abstract

The only NMR active oxygen isotope, Oxygen-17($^{17}\text{O}$ ), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, $^{17}\text{O}$ solid-state NMR offers unique insights into local structures and finds significant applications in the study of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of $^{17}\text{O}$ solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and Castep to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have collected over 7100 binary, ternary, and quaternary compounds from the Materials Project and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the $^{17}\text{O}$ NMR using machine learning techniques, further enhancing our ability to predict and understand $^{17}\text{O}$ NMR parameters in oxide crystals.