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

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Abstract
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.
Funding Information
  • Schweizerischer Nationalfonds zur F??rderung der Wissenschaftlichen Forschung (180544)
  • Schweizerischer Nationalfonds zur F??rderung der Wissenschaftlichen Forschung (225147)

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