深度学习及其在核磁共振波谱学中的应用

IF 7.3 2区化学 Q2 CHEMISTRY, PHYSICAL

Progress in Nuclear Magnetic Resonance Spectroscopy Pub Date : 2025-01-17 DOI:10.1016/j.pnmrs.2024.101556

Yao Luo, Xiaoxu Zheng, Mengjie Qiu, Yaoping Gou, Zhengxian Yang, Xiaobo Qu, Zhong Chen, Yanqin Lin

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引用次数: 0

摘要

核磁共振作为一项先进的技术，在化学、生物、医学等各个领域有着广泛的应用。然而，多维光谱采集时间长、灵敏度低等问题限制了核磁共振的广泛应用。传统算法旨在解决这些问题，但在速度和准确性方面存在局限性。深度学习（DL）是人工智能（AI）技术的一个分支，在包括核磁共振在内的许多领域都取得了显著的成功。本文概述了深度学习的基础知识和目前深度学习在核磁共振中的应用，强调了存在的挑战，并提出了潜在的改进方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Deep learning and its applications in nuclear magnetic resonance spectroscopy

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Deep learning and its applications in nuclear magnetic resonance spectroscopy

Nuclear Magnetic Resonance (NMR), as an advanced technology, has widespread applications in various fields like chemistry, biology, and medicine. However, issues such as long acquisition times for multidimensional spectra and low sensitivity limit the broader application of NMR. Traditional algorithms aim to address these issues but have limitations in speed and accuracy. Deep Learning (DL), a branch of Artificial Intelligence (AI) technology, has shown remarkable success in many fields including NMR. This paper presents an overview of the basics of DL and current applications of DL in NMR, highlights existing challenges, and suggests potential directions for improvement.

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来源期刊

Progress in Nuclear Magnetic Resonance Spectroscopy 物理-光谱学

CiteScore

14.30

自引率

8.20%

发文量

审稿时长

62 days

期刊介绍： Progress in Nuclear Magnetic Resonance Spectroscopy publishes review papers describing research related to the theory and application of NMR spectroscopy. This technique is widely applied in chemistry, physics, biochemistry and materials science, and also in many areas of biology and medicine. The journal publishes review articles covering applications in all of these and in related subjects, as well as in-depth treatments of the fundamental theory of and instrumental developments in NMR spectroscopy.