articleApplied Physics ReviewsMar 1, 2026Closed access

Recent progress in artificial intelligence enabled NMR spectroscopy: Methodologies, implementations, quality assessments, and prospects

HZHaolin ZhanYHYuqing HuangZCZhong Chen

Hefei University of Technology · Xiamen University · +1 more institution

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Abstract

Nuclear magnetic resonance (NMR) spectroscopy is widely used across chemistry, applied physics, life sciences, and related disciplines. As NMR studies grow in complexity, artificial intelligence (AI) has emerged as a transformative tool to improve NMR data acquisition, processing, and analysis, fundamentally reshaping conventional NMR workflows. This review provides a comprehensive overview of recent advances in AI-enabled NMR reconstruction, tracing its methodological evolution from early artificial neural networks and evolutionary algorithms to contemporary deep learning (DL) frameworks. Main applications are examined in detail, including sparse reconstruction, noise filtering and artifact suppression,…

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5
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73.83
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100%
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229
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3

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Topics
Keywords
  • Artifact (error)
  • Generalizability theory
  • Quality (philosophy)
  • Deep learning
  • Benchmarking
  • Artificial neural network
  • Signal processing
  • Nuclear magnetic resonance spectroscopy
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