Concluding remarks: Faraday Discussion on NMR crystallography

Area of Science:

• Solid-state chemistry and materials science
• Physical chemistry
• Computational chemistry

Background:

• Nuclear Magnetic Resonance (NMR) crystallography is a powerful technique for determining atomic structures.
• Recent advancements have expanded its capabilities to more complex and dynamic systems.
• The field is increasingly integrating computational and data-driven approaches.

Purpose of the Study:

• To review recent developments in NMR crystallography.
• To discuss the application of NMR crystallography to disordered, amorphous, and dynamic systems.
• To explore the role of machine learning and big data in NMR crystallography.

Main Methods:

• Experimental NMR spectroscopy
• Theoretical calculations and modeling
• Machine learning algorithms for data analysis and structure generation

Main Results:

• NMR crystallography provides rich information on a wide range of systems.
• Machine learning shows promise for handling large datasets and complex structures.
• Automation and best practices are crucial for exploiting the full potential of the technique.

Conclusions:

• NMR crystallography is a versatile tool for characterizing complex materials.
• Future directions involve enhanced automation, advanced machine learning, and interdisciplinary collaboration.
• Addressing challenges in data handling and model generation is key for future progress.