Jopa Sylwia, Bukowicki Marek, Shchukina Alexandra, Kazimierczuk Krzysztof
Centre of New Technologies, University of Warsaw, Banacha 2C, 02-097 Warsaw, Poland.
Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland.
J Phys Chem B. 2025 Jun 19;129(24):6078-6092. doi: 10.1021/acs.jpcb.5c02632. Epub 2025 Jun 4.
Despite commonly applied corrections, known as shimming, the magnetic field in an NMR spectrometer is never perfectly homogeneous. This undesired effect distorts the lineshapes, degrades the resolution, and lowers the signal-to-noise ratio in the collected spectra. As a remedy, numerical techniques have been developed to correct the spectra after acquisition, with reference deconvolution being the most popular example. However, these methods require a precarious parameter guess from the user, making them inconvenient and prone to errors. In particular, the imperfections of the reference deconvolution manifest themselves as strong oscillations at the spectral baseline. We propose a postacquisition shimming tool named ShimNet based on a convolutional neural network with the attention mechanism. The model learns the distortion characteristics of a given spectrometer from a series of calibration measurements. Once trained, it can correct any spectrum from the same machine in a fully automatic way. It achieves slightly better reconstruction quality than the existing methods and is considerably faster. This makes ShimNet an excellent tool for laboratories performing routine and massive NMR measurements for chemists. As an exemplary application, we demonstrate that the model properly corrects liquid-state spectra of small molecules, such as azarone, styrene, Cresol Red, or sodium butyrate. The open-source Python code is freely available from https://github.com/center4ml/shimnet and as the web service at https://huggingface.co/spaces/NMR-CeNT-UW/ShimNet.
尽管通常会进行称为匀场的校正，但核磁共振光谱仪中的磁场永远不会完全均匀。这种不良影响会使线形失真，降低分辨率，并降低所采集光谱的信噪比。作为一种补救措施，人们开发了一些数值技术，以便在采集后对光谱进行校正，参考去卷积是最常见的例子。然而，这些方法需要用户进行不稳定的参数猜测，这使得它们使用不便且容易出错。特别是，参考去卷积的缺陷表现为光谱基线处的强烈振荡。我们提出了一种基于带有注意力机制的卷积神经网络的采集后匀场工具，名为ShimNet。该模型从一系列校准测量中学习给定光谱仪的失真特征。一旦训练完成，它就可以以全自动方式校正同一台仪器的任何光谱。它实现的重建质量比现有方法略好，而且速度要快得多。这使得ShimNet成为为化学家进行常规和大量核磁共振测量的实验室的优秀工具。作为一个示例性应用，我们证明该模型能够正确校正小分子的液态光谱，如氮杂环丁烷、苯乙烯、甲酚红或丁酸钠。开源的Python代码可从https://github.com/center4ml/shimnet免费获取，也可作为网络服务在https://huggingface.co/spaces/NMR-CeNT-UW/ShimNet上获取。
