Machine learning in NMR spectroscopy.

Area of Science:

• Analytical Chemistry
• Biophysics
• Materials Science

Background:

• Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for analyzing molecular structures, dynamics, and interactions.
• Increasing complexity in NMR studies necessitates advanced computational approaches.
• Machine learning (ML) offers promising solutions for improving NMR data acquisition, processing, and analysis.

Purpose of the Study:

• To review recent advancements in the integration of machine learning with NMR spectroscopy.
• To highlight common ML applications within NMR spectroscopy.
• To identify trends and future directions at the intersection of ML and NMR.

Main Methods:

• Literature review of recent findings on ML in NMR spectroscopy.
• Categorization of ML applications in NMR (e.g., signal detection, chemical shift assignment, structure determination, chemical shift prediction, non-uniform sampling reconstruction, denoising).
• Discussion of ML methods, design choices, and data repositories for each application.

Main Results:

• ML is successfully applied to various NMR tasks, including signal detection, assignment, and structure determination.
• ML methods improve efficiency and accuracy in processing and analyzing complex NMR data.
• Key ML approaches and relevant data repositories are identified for common NMR applications.

Conclusions:

• Machine learning is a transformative technology for NMR spectroscopy.
• Further integration of ML will accelerate discoveries in molecular structure, dynamics, and interactions.
• Emerging trends suggest continued innovation in ML-driven NMR research.