Pure isotropic proton NMR spectra in solids using deep learning

The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two-dimensional set of magic-angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high-resolution 1H solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range.

质子固体核磁共振（proton solid-state NMR）谱的分辨率通常受自旋间偶极相互作用导致的谱峰展宽限制。魔角旋转（magic-angle spinning）通过引入相干平均效应，可缓解此类展宽问题。然而，即便使用当前实验条件下可达到的最高旋转速率，也无法完全消除自旋间的偶极相互作用。本文提出一种深度学习方法，可从不同旋转速率下采集的二维魔角旋转谱集中，还原得到纯各向同性质子谱。将该模型应用于8种有机固体样本，可得到各向同性线宽处于50~400 Hz区间的高分辨率¹H固体核磁共振谱。