From Spectra to Structure: AI-Powered ³¹P NMR Interpretation

Phosphorus-31 nuclear magnetic resonance (³¹P NMR) spectroscopy is a powerful technique for characterizing phosphorus-containing compounds in diverse chemical environments. However, spectral interpretation remains a time-consuming and expertise-dependent task, relying on reference tables and empirical comparisons. In this study, we introduce a data-driven approach that automates ³¹P NMR spectral analysis, providing rapid and accurate predictions of the local phosphorus environments. By leveraging a curated data set of experimental and synthetic spectra, our model achieves a Top-1 accuracy of 53.64% and a Top-5 accuracy of 77.69% at predicting the local environment around a phosphorus atom. Furthermore, it demonstrates robustness across different solvent conditions and outperforms expert chemists by 25% in spectral assignment tasks. The models, data sets, and architecture are openly available, facilitating seamless adoption in chemical laboratories engaged in structure elucidation, with the goal of advancing ³¹P NMR spectral analysis and interpretation.