If machines can learn, who needs scientists?

Area of Science:

• Nuclear Magnetic Resonance (NMR) Spectroscopy
• Computational Chemistry
• Data Science

Background:

• Machine learning (ML) has a long history in NMR applications.
• Recent advancements suggest a period of significant growth for ML in NMR.
• Existing public NMR data repositories are underutilized for ML training.

Purpose of the Study:

• To explore the future potential of machine learning in NMR spectroscopy.
• To identify and discuss key challenges hindering ML adoption in NMR.
• To propose potential strategies for overcoming these obstacles.

Main Methods:

• Perspective and speculative analysis of ML in NMR.
• Review of current challenges in data availability and interpretability.
• Discussion of integrating ML into hypothesis-driven NMR research.

Main Results:

• Significant growth in ML applications for NMR is anticipated.
• Key hurdles include limited training data and result interpretation.
• Integrating ML into hypothesis-driven research remains a challenge.

Conclusions:

• Machine learning holds immense potential to advance NMR spectroscopy.
• Addressing data limitations and interpretability is crucial for widespread adoption.
• Strategic approaches are needed to fully leverage ML in NMR research.