Machine learning assisted interpretation of 2D solid-state nuclear magnetic resonance spectra

A machine learning methodology using deep neural network (DNN) for interpreting multidimensional solid-state nuclear magnetic resonance (SSNMR) of various synthetic and natural polymers is presented. The separated local field (SLF) SSNMR which correlates local well-defined heteronuclear dipolar with the tensor orientation of the chemical shift anisotropy (CSA) of spin in the solid state can provide valuable structure and molecular dynamics information of synthetic and biopolymers. Compared with the traditional linear least-square fitting, the proposed DNN-based methodology can efficiently and accurately determine the tensor orientation of CSA of both C-13 and N-15 in all four samples. The method achieves prediction precisions of the Euler angles with < +/- 5 degrees and is characterized by low training costs and high efficiency (< 1 s). The feasibility and robustness of the DNN-based analysis methodology are confirmed by comparison to reported-literature values. This strategy is expected to aid in the interpretation of complex multidimensional NMR spectra of complicated polymer system. (C) 2023 Elsevier Inc. All rights reserved.

Automated summary

A machine learning methodology using deep neural network (DNN) is presented for interpreting multidimensional solid-state nuclear magnetic resonance (SSNMR) of synthetic and natural polymers. The proposed DNN-based method efficiently determines the tensor orientation of CSA of both C-13 and N-15, providing valuable structure and molecular dynamics information. The method achieves high prediction precision with low training costs and high efficiency.